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Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms pp 217–317Cite as

Emerging Biology-based CI Algorithms

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 62))

Abstract

In this chapter, a group of (more specifically 56 in total) emerging biology-based computational intelligence (CI) algorithms are introduced. We first, in Sect. 17.1, describe the organizational structure of this chapter. Then, from Sects. 17.2 to 17.57, each section is dedicated to a specific algorithm which falls within this category, respectively. The fundamentals of each algorithm and their corresponding performances compared with other CI algorithms can be found in each associated section. Finally, the conclusions drawn in Sect. 17.58 closes this chapter.

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References

  • Abdullah, S., Turabieh, H., & Mccollum, B. (2009). A hybridization of electromagnetic-like mechanism and great deluge for examination timetabling problems. Hybrid Metaheuristics, LNCS (Vol. 5818, pp. 60–72). Berlin: Springer.

    Google Scholar 

  • Abernethy, B., Kippers, V., Hanrahan, S. J., Pandy, M. G., Mcmanus, A. M., & Mackinnon, L. (2013). Biophysical foundations of human movement,, Champaign: Human Kinetics. ISBN 978-1-4504-3165-1.

    Google Scholar 

  • Acebo, E. D., & Rosa, J. L. D. L. (2008, April 1–4). Introducing bar systems: A class of swarm intelligence optimization algorithms. In AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications, University of Aberdeen (pp. 18–23). The Society for the Study of Artificial Intelligence and Simulation of Behaviour.

    Google Scholar 

  • Acquaah, G. (2012). Principles of plant genetics and breeding. River Street: Wiley. ISBN 978-0-470-66476-6.

    Google Scholar 

  • Adair, J. (2007). Develop your leadership skills. London: Kogan Page Limited. ISBN 0-7494-4919-5.

    Google Scholar 

  • Adamatzky, A., & Oliveira, P. P. B. D. (2011). Brazilian highways from slime mold’s point of view. Kybernetes, 40, 1373–1394.

    Google Scholar 

  • Adams, S. (2004). World War I. London: Dorling Kindersley Limited. ISBN 1-4053-0298-4.

    Google Scholar 

  • Ahrari, A., & Atai, A. A. (2010). Grenade explosion method: A novel tool for optimization of multimodal functions. Applied Soft Computing, 10, 1132–1140.

    Google Scholar 

  • Ahrari, A., Shariat-Panahi, M., & Atai, A. A. (2009). GEM: a novel evolutionary optimization method with improved neighborhood search. Applied Mathematics and Computation, 210, 379–386.

    MathSciNet  Google Scholar 

  • Al-Milli, N. R. (2010). Hybrid genetic algorithms with great deluge for course timetabling. International Journal of Computer Science and Network Security, 10, 283–288.

    Google Scholar 

  • Alatas, B. (2011). Photosynthetic algorithm approaches for bioinformatics. Expert Systems with Applications, 38, 10541–10546.

    Google Scholar 

  • Aleksiev, A. S., Longdon, B., Christmas, M. J., Sendova-Franks, A. B., & Franks, N. R. (2008). Individual and collective choice: Parallel prospecting and mining in ants. Naturwissenschaften, 95, 301–305.

    Google Scholar 

  • Alexiou, A., & Vlamos, P. (2012). A cultural algorithm for the representation of mitochondrial population. Advances in Artificial Intelligence, 2012, 1–7.

    Google Scholar 

  • Alonso, C., Herrera, C. M., & Ashman, T.-L. (2012). A piece of the puzzle: A method for comparing pollination quality and quantity across multiple species and reproductive events. New Phytologist, 193, 532–542.

    Google Scholar 

  • Aman, B. (2009). Spatial dynamic structures and mobility in computation. Unpublished Doctoral Thesis, Romania Academy.

    Google Scholar 

  • Anandaraman, C. (2011). An improved sheep flock heredity algorithm for job shop scheduling and flow shop scheduling. International Journal of Industrial Engineering Computations, 2, 749–764.

    Google Scholar 

  • Anandaraman, C., Sankar, A. M., & Natarajan, R. (2012). Evolutionary approaches for scheduling a flexible manufacturing system with automated guided vehicles and robots. International Journal of Industrial Engineering Computations, 3, 627–648.

    Google Scholar 

  • Ardjmand, E., & Amin-naseri, M. R. (2012). Unconscious search: A new structured search algorithm for solving continuous engineering optimization problems based on the theory of psychoanalysis. In Y. Tan, Y. Shi, & Z. Ji (Eds.), ICSI 2012, Part I, LNCS (Vol. 7331, pp. 233–242). Berlin: Springer.

    Google Scholar 

  • Ashby, L. H. & Yampolskiy, R. V. (2011). Genetic algorithm and wisdom of artificial crowds algorithm applied to light up. In 16th International Conference on Computer Games (GAMES 2011), (pp. 27–32). IEEE.

    Google Scholar 

  • Badgerow, J. P., & Hainsworth, F. R. (1981). Energy savings through formation flight? A re-examination of the vee formation. Journal of Theoretical Biology, 93, 41–52.

    Google Scholar 

  • Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., et al. (2008). Empirical investigation of starling flock: A benchmark study in collective animal behaviour. Animal Behaviour, 76, 201–215.

    Google Scholar 

  • Bater, L. (2007). Incredible insects: Answers to questions about miniature marvels. Vero Beach: Rourke Publishing LLC. Post Office Box 3328. ISBN 978-1-60044-348-0.

    Google Scholar 

  • Batista, L. D. S., Guimarães, F. G., & Ramírez, J. A. (2009). A distributed clonal selection algorithm for optimization in electromagnetics. IEEE Transactions on Magnetics, 45, 1598–1601.

    Google Scholar 

  • Bell, W. J., Roth, L. M., & Nalepa, C. A. (2007). Cockroaches: Ecology, behavior, and natural history. Maryland: The Johns Hopkins University Press. ISBN 978-0-8018-8616-4.

    Google Scholar 

  • Bellaachia, A., & Bari, A. (2012). Flock by leader: A novel machine learning biologically inspired clustering algorithm. In Y. Tan, Y. Shi, & Z. Ji (Eds.), ICSI 2012, Part I, LNCS (Vol. 7332, pp. 117–126). Berlin: Springer.

    Google Scholar 

  • Bhugra, D., Ruiz, P., & Gupta, S. (2013). Leadership in psychiatry. Hoboken: Wiley. ISBN 978-1-119-95291-6.

    Google Scholar 

  • Bolstad, T. M. (2012). Brownian motion. Department of Physics and Technology, University of Bergen.

    Google Scholar 

  • Brierley, A. S., & Cox, M. J. (2010). Shapes of krill swarms and fish schools emerge as aggregation members avoid predators and access oxygen. Current Biology, 20, 1758–1762.

    Google Scholar 

  • Brownlee, J. (2007). Clonal selection algorithms. CIS Technical Report, 070209A, 1–13.

    Google Scholar 

  • Burke, E., Bykov, Y., Newall, J., & Petrovic, S. (2004). A time-predefined local search approach to exam timetabling problems. IIE Transactions, 3, 509–528.

    Google Scholar 

  • Campelo, F., Guimarães, F. G., Igarashi, H., & Ramírez, J. A. (2005). A clonal selection algorithm for optimization in electromagnetics. IEEE Transactions on Magnetics, 41, 1736–1739.

    Google Scholar 

  • Cao, C.-H., Wang, L.-M., Han, C.-Y., Zhao, D.-Z., & Zhang, B. (2012). Geese PSO optimization in geometric constraint solving. Information Technology Journal, 11, 504.

    Google Scholar 

  • Carlson, N. R. (2013). Physiology of behavior. New Jersey: Pearson Education, Inc. ISBN 978-0-205-23948-1.

    Google Scholar 

  • Carpentier, R. (2011). Photosynthesis research protocols. New York: Springer. ISBN 978-1-60671-924-6.

    Google Scholar 

  • Castro, L. N. D., & Zuben, F. J. V. (2000, July). The clonal selecton algorithm with engineering applications. In Workshop on Artificial Immune Systems and Their Applications, Las Vegas, USA, pp. 1–7.

    Google Scholar 

  • Castro, L. N. D., & Zuben, F. J. V. (2002). Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation, 6, 239–251.

    Google Scholar 

  • Chalmers, D. J. (2010). The character of consciousness. USA: Oxford University Press. ISBN 978-0-195-31111-2.

    Google Scholar 

  • Chandrasekaran, M., Asokan, P., Kumanan, S., & Balamurugan, T. (2006). Sheep flocks heredity model algorithm for solving job shop scheduling problems. International Journal of Applied Management and Technology, 4, 79–100.

    Google Scholar 

  • Chapman, R. F. (2013). The insects: structure and function. In S. J. Simpson & A. E. Douglas (Eds.). New York: Cambridge University Press. ISBN 978-0-521-11389-2.

    Google Scholar 

  • Chen, C.-C., & Lee, Y.-T. (Eds.). (2008). Leadership and management in China: Philosophies, theories, and practices. Cambridge: Cambridge University Press. ISBN 978-0-511-40909-7.

    Google Scholar 

  • Chen, K., Li, T., & Cao, T. (2006). Tribe-PSO: A novel global optimization algorithm and its application in molecular docking. Chemometrics and Intelligent Laboratory Systems, 82, 248–259.

    Google Scholar 

  • Chen, T. (2009). A simulative bionic intelligent optimization algorithm: Artificial searching swarm algorithm and its performance analysis. In IEEE International Joint Conference on Computational Sciences and Optimization (CSO) (pp. 864–866).

    Google Scholar 

  • Chen, T., Liu, Z., Shu, Q., & Zhang, L. (2009a). On the analysis of performance of the improved artificial searching swarm algorithm. In IEEE 2nd International Conference on Intelligent Networks and Intelligent Systems (ICINIS) (pp. 502–506).

    Google Scholar 

  • Chen, T., Pang, L., Du, J., Liu, Z., & Zhang, L. (2009b). Artificial searching swarm algorithm for solving constrained optimization problems. In IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS) (pp. 562–565) .

    Google Scholar 

  • Chen, T., Wang, Y., & Li, J. (2012). Artificial tribe algorithm and its performance analysis. Journal of Software, 7, 651–656.

    Google Scholar 

  • Chen, T., Wang, Y., Pang, L., Liu, Z., & Zhang, L. (2010a). An improved artificial searching swarm algortihm and its performance analysis. In IEEE 2nd International Conference on Computer Modeling and Simulation (ICCMS) (pp. 260–263).

    Google Scholar 

  • Chen, T., Zhang, L., Liu, Z., Pang, L., & Shu, Q. (2009c). On the analysis of performance of the artificial searching swarm algorithm. In IEEE 5h International Conference on Natural Computation (ICNC) (pp. 365–368).

    Google Scholar 

  • Chen, Y., Cui, Z., & Zeng, J. (2010b, July 7–9). Structural optimization of lennard-jones clusters by hybrid social cognitive optimization algorithm. In F. Sun, Y. Wang, J. Lu, B. Zhang, W. Kinsner, & L. A. Zadeh (Eds.), In 9th International Conference on Cognitive Informatics (ICCI) (pp. 204–208). IEEE. Beijing, China.

    Google Scholar 

  • Chen, Z., & Tang, H. (2010). Cockroach swarm optimization. In IEEE 2nd International Conference on Computer Engineering and Technology (ICCET) (pp. 652–655).

    Google Scholar 

  • Chen, Z., & Tang, H. (2011). Cockroach swarm optimization for vehicle routing problems. Energy Procedia, 13, 30–35.

    Google Scholar 

  • Cheng, L., Xu, Y.-H., Zhang, H.-B., Qian, Z.-L., & Feng, G. (2010). New bionics optimization algorithm: food truck-cockroach swarm optimization algorithm (in Chinese). Computer Engineering, 36, 208–209.

    Google Scholar 

  • Ciobanu, G., Desai, R., & Kumar, A. (2003). Membrane systems and distributed computing. In G. Păun (Ed.), WMC-CdeA 2002, LNCS (Vol. 2597, pp. 187–202). Berlin: Springer.

    Google Scholar 

  • Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers and Geosciences, 46, 229–247.

    Google Scholar 

  • Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219, 8121–8144.

    MathSciNet  Google Scholar 

  • Coelho, L. D. S., & Bernert, D. L. D. A. (2009). PID control design for chaotic synchronization using a tribes optimization approach. Chaos, Solitons and Fractals, 42, 634–640.

    Google Scholar 

  • Conradt, L., & List, C. (2009). Group decisions in humans and animals: A survey. Philosophical Transaction of the Royal Society B, 364, 719–742.

    Google Scholar 

  • Cordoni, G. (2009). Social play in captive wolves (Canis lupus): Not only an immature affair. Behaviour, 146, 1363–1385.

    Google Scholar 

  • Couzin, I. D. (2009). Collective cognition in animal groups. Trends in Cognitive Sciences, 13, 36–43.

    Google Scholar 

  • Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005). Effective leadership and decision-making making in animal groups on the move. Nature, 434, 513–516.

    Google Scholar 

  • Creel, S. (1997). Cooperative hunting and group size: Assumptions and currencies. Animal Behaviour, 54, 1319–1324.

    Google Scholar 

  • Cuevas, E., Cienfuegos, M., Zaldívar, D., & Pérez-Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications. doi: http://dx.doi.org/10.1016/j.eswa.2013.05.041.

  • Cuevas, E., Zaldívar, D., & Pérez-Cisneros, M. (2013b). A swarm optimization algorithm for multimodal functions and its application in multicircle detection. Mathematical Problems in Engineering, 2013, 1–22.

    Google Scholar 

  • Cui, X., Gao, J., & Potok, T. E. (2006). A flocking based algorithm for document clustering analysis. Journal of Systems Architecture, 52, 505–515.

    Google Scholar 

  • Cui, Y., Guo, R., & Guo, D. (2009). A naïve five-element string algorithm. Journal of Software, 4, 925–934.

    Google Scholar 

  • Cui, Y. H., Guo, R., Rao, R. V., & Savsani, V. J. (2008, December 15–17) Harmony element algorithm: A naive initial searching range. In International Conference on Advances in Mechanical Engineering, S.V. (pp. 1–6). National Institute of Technology, Gujarat, India.

    Google Scholar 

  • Cui, Z., & Cai, X. (2010, July 7–9). Using social cognitive optimization algorithm to solve nonlinear equations. In F. Sun, Y. Wang, J. Lu, B. Zhang, W. Kinsner & L. A. Zadeh (Eds.), In 9th International Conference on Cognitive Informatics (ICCI) (pp. 199–203). Beijing, China. IEEE.

    Google Scholar 

  • Cui, Z., Cai, X., & Shi, Z. (2011). Social emotional optimization algorithm with group decision. Scientific Research and Essays, 6, 4848–4855.

    Google Scholar 

  • Cui, Z., Shi, Z., & Zeng, J. (2010). Using social emotional optimization algorithm to direct orbits of chaotic systems. In B. K. Panigrahi, S. Das, P. N. Suganthan & S. S. Dash (Eds.), Swarm, Evolutionary, and Memetic Computing, LNCS (Vol. 6466, pp. 389–395). Berlin: Springer.

    Google Scholar 

  • Cui, Z., Xu, Y., & Zeng, J. (2012). Social emotional optimization algorithm with random emotional selection strategy. In R. Parpinelli (Ed.), Theory and New Applications of Swarm Intelligence, Chap. 3 (pp. 33–50). Croatia: InTech. ISBN 978-953-51-0364-6.

    Google Scholar 

  • Cummins, B., Cortez, R., Foppa, I. M., Walbeck, J., & Hyman, J. M. (2012). A spatial model of mosquito host-seeking behavior. PLoS Computational Biology, 8, 1–13.

    MathSciNet  Google Scholar 

  • Cutts, C. J., & Speakman, J. R. (1994). Energy savings in formation flight of pink-footed geese. Journal of Experimental Biology, 189, 251–261.

    Google Scholar 

  • Dai, C., Chen, W., & Zhu, Y. (2006, November). Seeker optimization algorithm. In IEEE International Conference on Computational Intelligence and Security (pp. 225–229). Guangzhou, China.

    Google Scholar 

  • Dai, C., Chen, W., Song, Y., & Zhu, Y. (2010a). Seeker optimization algorithm: A novel stochastic search algorithm for global numerical optimization. Journal of Systems Engineering and Electronics, 21, 300–311.

    Google Scholar 

  • Dai, C., Chen, W., & Zhu, Y. (2010b). Seeker optimization algorithm for digital IIR filter design. IEEE Transactions on Industrial Electronics, 57, 1710–1718.

    Google Scholar 

  • Dai, C., Chen, W., Zhu, Y., & Zhang, X. (2009a). Reactive power dispatch considering voltage stability with seeker optimization algorithm. Electric Power Systems Research, 79, 1462–1471.

    Google Scholar 

  • Dai, C., Chen, W., Zhu, Y., & Zhang, X. (2009b). Seeker optimization algorithm for optimal reactive power dispatch. IEEE Transactions on Power Systems, 24, 1218–1231.

    Google Scholar 

  • Dai, C., Zhu, Y., & Chen, W. (2007). Seeker optimization algorithm. In Y. Wang, Y. Cheung & H. Liu (Eds.), CIS 2006, LNAI (Vol. 4456. pp. 167–176). Berlin: Springer.

    Google Scholar 

  • Dai, S., Zhuang, P., & Xiang, W. (2013). GSO: An improved PSO based on geese flight theory. In Y. Tan, Y. Shi, & H. Mo (Eds.), Advances in Swarm Intelligence, ICSI 2013, Part I, LNCS (Vol. 7928, pp. 87–95). Berlin: Springer.

    Google Scholar 

  • Daskin, A., & Kais, S. (2011). Group leaders optimization algorithm. Molecular Physics, 109, 761–772.

    Google Scholar 

  • Davendra, D., & Zelinka, I. (2009). Optimization of quadratic assignment problem using self-organinsing migrating algorithm. Computing and Informatics, 28, 169–180.

    Google Scholar 

  • Davendra, D., Zelinka, I., Bialic-Davendra, M., Senkerik, R., & Jasek, R. (2013). Discrete self-organising migrating algorithm for flow-shop scheduling with no-wait makespan. Mathematical and Computer Modelling, 57, 100–110.

    MathSciNet  Google Scholar 

  • Digalakis, J. G., & Margaritis, K. G. (2002). A multipopulation cultural algorithm for the electrical generator scheduling problem. Mathematics and Computers in Simulation, 60, 293–301.

    MATH  MathSciNet  Google Scholar 

  • Ding, S., & Li, S. (2009). Clonal selection algorithm for feature selection and parameters optimization for support vector machines. In IEEE 2nd International Symposium on Knowledge Acquisition and Modeling (pp. 17–20).

    Google Scholar 

  • Du, Y., Li, H., Pei, Z., & Peng, H. (2005). Intelligent spider’s algorithm of search engine based on keyword. ECTI Transactions on Computer and Information Theory, 1, 40–49.

    Google Scholar 

  • Dubinsky, Z. (Ed.). (2013). Photosynthesis. InTech: Croatia. ISBN 978-953-51-1161-0.

    Google Scholar 

  • Dueck, G. (1993). New optimization heuristics: The great deluge algorithm and the record-to-record travel. Journal of Computational Physics, 104, 86–92.

    MATH  Google Scholar 

  • Duman, E., Uysal, M., & Alkaya, A. F. (2012). Migrating birds optimization: A new metaheuristic approach and its performance on quadratic assignment problem. Information Sciences, 217, 65–77.

    MathSciNet  Google Scholar 

  • Durrett, R. (1984). Brownian motion and martingales in analysis. Belmont: Wadsworth Advanced Books and Software, A Division of Wadsworth, Inc. ISBN 0-534-03065-3.

    Google Scholar 

  • Ebensperger, L. A. (2001). A review of the evolutionary causes of rodent group-living. Acta Theriologica, 46, 115–144.

    Google Scholar 

  • Eckstein, M. P., Das, K., Pham, B. T., Peterson, M. F., Abbey, C. K., Sy, J. L., et al. (2012). Neural decoding of collective wisdom with multi-brain computing. NeuroImage, 59, 94–108.

    Google Scholar 

  • Feng, X., Lau, F. C. M., & Gao, D. (2009). A new bio-inspired approach to the traveling salesman problem. In J. Zhou (Ed.), Complex 2009, Part II, LNICST, (Vol. 5, pp. 1310–1321). Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

    Google Scholar 

  • Frank, S. A. (1998). Foundations of social evolution. New Jersey: Princeton University Press. ISBN 0-691-05933-0.

    Google Scholar 

  • Fuller, T. K., Mech, L. D., & Cockrane, J. F. (2003). Wolf population dynamics. In L. D. Mech & L. Boitani (Eds.), Wolves: Behavior, Ecology and Conservation (pp. 161–191). Chicago: University of Chicago Press.

    Google Scholar 

  • Gamlin, L. (2009). Evolution. New York: Dorling Kindersley Limited. ISBN 978-0-7566-5028-5.

    Google Scholar 

  • Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17, 4831–1845.

    MATH  MathSciNet  Google Scholar 

  • Gandomi, A. H., Yang, X.-S., Talatahari, S., & Deb, S. (2012). Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization. Computers and Mathematics with Applications, 63, 191–200.

    MATH  MathSciNet  Google Scholar 

  • Gao, S., Chai, H., Chen, B., & Yang, G. (2013). Hybrid gravitational search and clonal selection algorithm for global optimization. In Y. Tan, Y. Shi & H. Mo (Eds.), Advances in Swarm Intelligence, LNCS (Vol. 7929, pp. 1–10). Hybrid gravitational search and clonal selection algorithm for global optimization. Berlin: Springer.

    Google Scholar 

  • Ghatei, S., Khajei, R. P., Maman, M. S., & Meybodi, M. R. (2012). A modified PSO using great deluge algorithm for optimization. Journal of Basic and Applied Scientific Research, 2, 1362–1367.

    Google Scholar 

  • Gheorghe, M., Păun, G., Rozenberg, G., Salomaa, A., & Verlan, S. (Eds.). (2012). Membrane computing. Berlin: Springer. ISBN 978-3-642-28023-8.

    MATH  Google Scholar 

  • Giraldeau, L.-A., Soos, C., & Beauchamp, G. (1994). A test of the producer-scrounger foraging game in captive flocks of spice finches, Lonchura punctulata. Behavioral Ecology and Sociobiology, 34, 251–256.

    Google Scholar 

  • Goffredo, S., & Dubinsky, Z. (2014). The Mediterranean Sea: Its history and present challenges. New York: Springer. ISBN 978-94-007-6703-4.

    Google Scholar 

  • Gofman, Y. (2012). Computational studies of the interactions of biologically active peptides with membrane. Unpublished Doctoral Thesis, Universität Hamburg.

    Google Scholar 

  • Gross, R. (2014). Psychology: The science of mind and behaviour. London: Hodder Education. ISBN 978-1-4441-0831-6.

    Google Scholar 

  • Gusset, M., & Macdonald, D. W. (2010). Group size effects in cooperatively breeding African wild dogs. Animal Behaviour, 79, 425–428.

    Google Scholar 

  • Hagler, G. (2013). Modelinig ships and space craft. Berlin: Springer. ISBN 978-1-4614-4595-1.

    Google Scholar 

  • Hasançebi, O., & Azad, S. K. (2012). An efficient metaheuristic algorithm for engineering optimization: SPOT. International Journal of Optimization in Civil Engineering, 2, 479–487.

    Google Scholar 

  • Havens, T. C., Spain, C. J., Salmon, N. G., & Keller, J. M. (2008, September 21–23). Roach infestation optimization. In IEEE Swarm Intelligence Symposium (pp. 1–7). St. Louis MO, USA.

    Google Scholar 

  • Heylighen, F. (1992). Evolution, selfishness and cooperation. Journal of Ideas, 2, 70–76.

    Google Scholar 

  • Hobbs, R. J., Higgs, E. S., & Hall, C. M. (2013). Novel ecosystems: Intervening in the new ecological world order. Hoboken: Wiley. ISBN 978-1-118-35422-3.

    Google Scholar 

  • Howell, D. C. (2014). Fundamental statistics for the behavioral sciences. Belmont: Cengage Learning. ISBN 978-1-285-07691-1.

    Google Scholar 

  • Irvine, E. (2013). Consciousness as a scientific concept: A philosophy of science perspective. Dordrecht: Springer. ISBN 978-94-007-5172-9.

    Google Scholar 

  • Ishdorj, T.-O. (2006). Membrane computing, neural inspirations, gene assembly in Ciliates. Unpublished Doctoral Thesis, University of Seville.

    Google Scholar 

  • Janecek, A., & Tan, Y. (2011). Swarm intelligence for non-negative matrix factorization. International Journal of Swarm Intelligence Research, 2, 12–34.

    Google Scholar 

  • Jelinek, R. (2013). Biomimetics: A molecular perspective. Berlin/Boston: Walter de Gruyter. ISBN 978-3-11-028117-0.

    Google Scholar 

  • Jonassen, T. M. (2006, June). Symbolic dynamics, the spider algorithm and finding certain real zeros of polynomials of high degree. In 8th International Mathematica Symposium (pp. 1–16). Avignon.

    Google Scholar 

  • Karci, A. (2007a). Human being properties of saplings growing up algorithm. In International Conference on Computational Cybernetics (ICCC) (pp. 227–232). IEEE.

    Google Scholar 

  • Karci, A. (2007b). Natural inspired computational intelligence method: saplings growing up algorithm. In International Conference on Computational Cybernetics (ICCC), pp. 221–226. IEEE.

    Google Scholar 

  • Karci, A. (2007c). Theory of saplings growing up algorithm. In Adaptive and Natural Computing Algorithms, LNCS (Vol. 4431, pp. 450–460). Berlin: Springer.

    Google Scholar 

  • Karci, A., & Alatas, B. (2006). Thinking capability of saplings growing up algorithm. In Intelligent Data Engineering and Automated Learning (IDEAL 2006), LNCS (Vol. 4224, pp. 386–393). Berlin: Springer.

    Google Scholar 

  • Kashan, A. H. (2009). League championship algorithm: a new algorithm for numerical function optimization. In IEEE International Conference of Soft Computing and Pattern Recognition (SoCPAR) (pp. 43–48).

    Google Scholar 

  • Kashan, A. H. (2011). An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA). Computer-Aided Design, 43, 1769–1792.

    Google Scholar 

  • Kashan, A. H., & Karimi, B. (2010, 18–23 July). A new algorithm for constrained optimization inspired by the sport league championships. In World Congress on Computational Intelligence (WCCI) (pp. 487–494). CCIB, Barcelona, Spain.

    Google Scholar 

  • Keller, L., & Gordon, É. (2009). The lives of ants (translated by James Grieve). Oxford: Oxford University Press Inc. ISBN 978–0–19–954186–7.

    Google Scholar 

  • Kim, H., & Ahn, B. (2001). A new evolutionary algorithm based on sheep flocks heredity model. In Conference on Communications, Computers and Signal Processing (pp. 514–517). IEEE.

    Google Scholar 

  • Kim, Y.-B. (2012). Distributed algorithms in membrane systems. Unpublished Doctoral Thesis, University of Auckland.

    Google Scholar 

  • King, A. J., Sueur, C., Huchard, E., & Cowlishaw, G. (2011). A rule-of-thumb based on social affiliation explains collective movements in desert baboons. Animal Behaviour, 82, 1337–1345.

    Google Scholar 

  • Krishnanand, K. R., Hasani, S. M. F., & Panigrahi, B. K. (2013). Optimal power flow solution using self-evolving brain–storming inclusive teaching-learning-based algorithm. In Y. Tan, Y. Shi, & H. Mo (Eds.), ICSI 2013, Part I, LNCS (Vol. 7928, pp. 338–345). Berlin: Springer.

    Google Scholar 

  • Kwasnicka, H., Markowska-Kaczmar, U., & Mikosik, M. (2011). Flocking behaviour in simple ecosystems as a result of artificial evolution. Applied Soft Computing, 11, 982–990.

    Google Scholar 

  • Lancaster, R., Butler, R. E. A., Lancaster, J. M., & Shimizu, T. (1998). Fireworks: Principles and practice. New York: Chemical Publishing Co., Inc. ISBN 0-8206-0354-6.

    Google Scholar 

  • Lee, D., & Quessy, S. (2003). Visual search is facilitated by scene and sequence familiarity in rhesus monkeys. Vision Research, 43, 1455–1463.

    Google Scholar 

  • Lemasson, B. H., Anderson, J. J., & Goodwin, R. A. (2009). Collective motion in animal groups from a neurobiological perspective: The adaptive benefits of dynamic sensory loads and selective attention. Journal of Theoretical Biology, 261, 501–510.

    MathSciNet  Google Scholar 

  • Levin, S. A. (2013a). Encyclopedia of biodiversity. Oxford: Academic Press, Elsevier Inc. ISBN 978-0-12-384719-5.

    Google Scholar 

  • Levin, S. A. (2013b). Encyclopedia of biodiversity. Oxford: Academic Press, Elsevier Inc. ISBN 978-0-12-384719-5.

    Google Scholar 

  • Levin, S. A. (2013c). Encyclopedia of biodiversity. Oxford: Academic Press, Elsevier Inc. ISBN 978-0-12-384719-5.

    Google Scholar 

  • Levin, S. A. (2013d). Encyclopedia of biodiversity. Oxford: Academic Press, Elsevier Inc. ISBN 978-0-12-384719-5.

    Google Scholar 

  • Levin, S. A. (2013e). Encyclopedia of biodiversity. Oxford: Academic Press, Elsevier Inc. ISBN 978-0-12-384719-5.

    Google Scholar 

  • Levin, S. A. (2013f). Encyclopedia of biodiversity,. London: Academic Press, Elsevier Inc. ISBN 978-0-12-384719-5.

    Google Scholar 

  • Li, K., Torres, C. E., Thomas, K., Rossi, L. F., & Shen, C.-C. (2011). Slime mold inspired routing protocols for wireless sensor networks. Swarm Intelligence, 5, 183–223.

    Google Scholar 

  • Li, Y. (2010, October 22–24). Solving TSP by an ACO-and-BOA-based hybrid algorithm. In IEEE International Conference on Computer Application and System Modeling (ICCASM), (Vol. 12, pp. 189–192).

    Google Scholar 

  • Lihoreau, M., Costa, J. T., & Rivault, C. (2012). The social biology of domiciliary cockroaches: colony structure, kin recognition and collective decisions. Insectes Sociaux. doi: 10.1007/s00040-012-0234-x.

  • Lihoreau, M., Deneubourg, J.-L., & Rivault, C. (2010). Collective foraging decision in a gregarious insect. Behavioral Ecology and Sociobiology, 64, 1577–1587.

    Google Scholar 

  • Lissaman, P. B. S., & Shollenberger, C. A. (1970). Formation flight of birds. Science, 168, 1003–1005.

    Google Scholar 

  • Liu, C., Yan, X., Liu, C., & Wu, H. (2011). The wolf colony algorithm and its application. Chinese Journal of Electronics, 20, 212–216.

    Google Scholar 

  • Liu, J. Y., Guo, M. Z., & Deng, C. (2006). Geese PSO: An efficient improvement to particle swarm optimization. Computer Science, 33, 166–168.

    Google Scholar 

  • Lu, Y., & Liu, X. (2011). A new population migration algorithm based on the chaos theory. In IEEE 2nd International Symposium on Intelligence Information Processing and Trusted Computing (IPTC) (pp. 147–10).

    Google Scholar 

  • Luo, X., Li, S., & Guan, X. (2010). Flocking algorithm with multi-target tracking for multi-agent systems. Pattern Recognition Letters, 31, 800–805.

    Google Scholar 

  • Maathuis, F. J. M. (2013). Plant mineral nutrients: Methods and protocols. New York: Springer. ISBN 978-1-62703-151-6.

    Google Scholar 

  • Macdonald, D. W., Creel, S., & Mills, M. G. L. (2004). Society: Canid society. In D. W. Macdonald & C. Sillero-Zubiri (Eds.), Biology and Conservation of Wild Carnivores (pp. 85–106). Oxford: Oxford University Press.

    Google Scholar 

  • Magstadt, T. M. (2013). Understanding politics: ideas, institutions, and issues. Cengage Learning: Belmont. ISBN 978-1-111-83256-8.

    Google Scholar 

  • Marcus, J. B. (2013). Culinary nutrition: The science and practice of healthy cooking. Waltham: Elsevier. ISBN 978-0-12-391882-6.

    Google Scholar 

  • Maroosi, A., & Muniyandi, R. C. (2013). Membrane computing inspired genetic algorithm on multi-core processors. Journal of Computer Science, 9, 264–270.

    Google Scholar 

  • Mayfield, J. E. (2013). The engine of complexity: Evolution as computation. New York: Columbia University Press. ISBN 978-0-231-16304-0.

    Google Scholar 

  • Mills, D. S., Marchant-Forde, J. N., McGreevy, P. D., Morton, D. B., Nicol, C. J., Phillips, C. J. C., et al. (Eds.). (2010). The encyclopedia of applied animal behaviour and welfare. Wallingford: CAB International. ISBN 978-0-85199-724-7.

    Google Scholar 

  • Mosser, A., & Packer, C. (2009). Group territoriality and the benefits of sociality in the African lion, Panthera leo. Animal Behaviour, 78, 359–370.

    Google Scholar 

  • Mucherino, A., & Seref, O. (2007). Monkey search: A novel metaheuristic search for global optimization. In AIP Conference Proceedings (Vol. 953, pp. 162–173).

    Google Scholar 

  • Mukherjee, R., Chakraborty, S., & Samanta, S. (2012). Selection of wire electrical discharge machining process parameters using non-traditional optimization algorithms. Applied Soft Computing, 12, 2506–2516.

    Google Scholar 

  • Müller, V. C. (Ed.). (2013). Philosophy and theory of artificial intelligence. Berlin: Springer. ISBN 978-3-642-31673-9.

    Google Scholar 

  • Muniyandi, R. C., & Zin, A. M. (2013). Membrane computing as the paradigm for modeling system biology. Journal of Computer Science, 9, 122–127.

    Google Scholar 

  • Murase, H. (2000). Finite element inverse analysis using a photosynthetic algorithm. Computers and Electronics in Agriculture, 29, 115–123.

    Google Scholar 

  • Muro, C., Escobedo, R., Spector, L., & Coppinger, R. P. (2011). Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Behavioural Processes, 88, 192–197.

    Google Scholar 

  • Murphy, N. (2010). Uniformity conditions for membrane system uncovering complexity below P. Unpublished Doctoral Thesis, National University of Ireland Maynooth.

    Google Scholar 

  • Nabil, E., Badr, A., & Farag, I. (2012). A membrane-immune algorithm for solving the multiple 0-1 knapsack problem (pp. 3–15). LVII: Informatica.

    Google Scholar 

  • Nahas, N., Nourelfath, M., & Aït-Kadi, D. (2010). Iterated great deluge for the dynamic facility layout problem. Canada: Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation, Report No.: CIRRELT-2010-20.

    Google Scholar 

  • Nakagaki, T., Yamada, H., & Tóth, Á. (2000). Maze-solving by an amoeboid organism. Nature, 407, 470.

    Google Scholar 

  • Nara, K., Takeyama, T., & Kim, H. (1999). A new evolutionary algorithm based on sheep flocks heredity model and its application to scheduling problem. In IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. VI-503–VI-508).

    Google Scholar 

  • Neshat, M., Sepidnam, G., & Sargolzaei, M. (2013). Swallow swarm optimization algorithm: A new method to optimization. Neural Computing & Application, 23, 429–454. doi: 10.1007/s00521-012-0939-9.

  • Newell, P. C. (1978). Genetics of the cellular slime molds. Annual Review of Genetics, 12, 69–93.

    Google Scholar 

  • Nguyen, V., Kearney, D., & Gioiosa, G. (2008). An implementation of membrane computing using reconfigurable hardware. Computing and Informatics, 27, 551–569.

    MathSciNet  Google Scholar 

  • Nicolis, S. C., Detrain, C., Demolin, D., & Deneubourg, J. L. (2003). Optimality of collective choices: a stochastic approach. Bulletin of Mathematical Biology, 65, 795–808.

    Google Scholar 

  • Niizato, T., & Gunji, Y.-P. (2011). Metric–topological interaction model of collective behavior. Ecological Modelling, 222, 3041–3049.

    Google Scholar 

  • Nishida, T. Y. (2005, July 18–21). Membrane algorithm: An approximate algorithm for NP-complete optimization problems exploiting P-systems. In R. Freund, G. Lojka, M. Oswald, & G. Păun (Eds.), In 6th International workshop on membrane computing (WMC) (pp. 26–43). Vienna, Austria. Institute of Computer Languages, Faculty of Informatics, Vienna University of Technology.

    Google Scholar 

  • Nolle, L., Zelinka, I., Hopgood, A. A., & Goodyear, A. (2005). Comparison of an self-organizing migration algorithm with simulated annealing and differential evolution for automated waveform tuning. Advanced Engineering Software, 36, 645–653.

    Google Scholar 

  • Oca, M. A. M. D., Ferrante, E., Scheidler, A., Pinciroli, C., Birattari, M., & Dorigo, M. (2011). Majority-rule opinion dynamics with differential latency: A mechanism for self-organized collective decision-making. Swarm Intelligence, 5, 305–327.

    Google Scholar 

  • Ochoa-Zezzatti, A., Bustillos, S., Jaramillo, R., & Ruíz, V. (2012). Improving practice sports in a largest city using a cultural algorithm. International Journal of Combinatorial Optimization Problems and Informatics, 3, 14–20.

    Google Scholar 

  • Oftadeh, R., Mahjoob, M. J., & Shariatpanahi, M. (2010). A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search. Computers and Mathematics with Applications, 60, 2087–2098.

    MATH  Google Scholar 

  • Onwubolu, G. C. (2006). Performance-based optimization of multi-pass face milling operations using Tribes. International Journal of Machine Tools and Manufacture, 46, 717–727.

    Google Scholar 

  • Packer, C., & Caro, T. M. (1997). Foraging costs in social carnivores. Animal Behaviour, 54, 1317–1318.

    Google Scholar 

  • Păun, G. (2000). Computing with membranes. Journal of Computer and System Sciences, 61, 108–143.

    MATH  MathSciNet  Google Scholar 

  • Păun, G. (2002). A guide to membrane computing. Theoretical Computer Science, 287, 73–100.

    MATH  MathSciNet  Google Scholar 

  • Păun, G. (2007). Tracing some open problems in membrane computing. Romanian Journal of Information Science and Technology, 10, 303–314.

    Google Scholar 

  • Pei, Y., Zheng, S., Tan, Y., & Takagi, H. (2012, October 14–17). An empirical study on influence of approximation approaches on enhancing fireworks algorithm. In IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2012) (pp. 1322–1327). Seoul, Korea.

    Google Scholar 

  • Petit, O., & Bon, R. (2010). Decision-making processes: The case of collective movements. Behavioural Processes, 84, 635–647.

    Google Scholar 

  • Picarougne, F., Azzag, H., Venturini, G., & Guinot, C. (2007). A new approach of data clustering using a flock of agents. Evolutionary Computation, 15, 345–367.

    Google Scholar 

  • Port, A. C., & Yampolskiy, R. V. (2012). Using a GA and wisdom of artificial crowds to solve solitaire battleship puzzles. In IEEE 17th International Conference on Computer Games (CGAMES 2012) (pp. 25–29).

    Google Scholar 

  • Premaratne, U., Samarabandu, J., & Sidhu, T. (2009, December 28–31). A new biologically inspired optimization algorithm. In IEEE 4th International Conference on Industrial and Information Systems (ICIIS) (pp. 279–284). Sri Lanka.

    Google Scholar 

  • Ramachandran, V. S. (2012a). Encyclopedia of human behavior. London: Elsevier. ISBN 978-0-12-375000-6.

    Google Scholar 

  • Ramachandran, V. S. (2012b). Encyclopedia of human behavior. London: Elsevier. ISBN 978-0-12-375000-6.

    Google Scholar 

  • Ramachandran, V. S. (2012c). Encyclopedia of human behavior. London: Elsevier. ISBN 978-0-12-375000-6.

    Google Scholar 

  • Rao, R. V., Vakharia, D. P., & Savsani, V. J. (2009). Mechanical engineering design optimisation using modified harmony elements algorithm. International Journal of Design Engineering, 2, 116–135.

    Google Scholar 

  • Ravi, V. (2004). Optimization of complex system reliability by a modified great deluge algorithm. Asia-Pacific Journal of Operational Research, 21, 487–497.

    MATH  MathSciNet  Google Scholar 

  • Ray, T., & Liew, K. M. (2003). Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation, 7, 386–396.

    Google Scholar 

  • Reece, J. B., Urry, L. A., Cain, M. L., Wasserman, S. A., Minorsky, P. V., & Jackson, R. B. (2011). Campbell biology. San Francisco: Pearson Education, Inc. ISBN 978-0-321-55823-7.

    Google Scholar 

  • Resende, R. R., & Ulrich, H. (2013). Trends in stem cell proliferation and cancer research. Dordrecht: Springer. ISBN 978-94-007-6210-7.

    Google Scholar 

  • Reynolds, C. W. (1987). Flocks, herds, and schools: A distributed behavioral model. Computer Graphics, 21, 25–34.

    Google Scholar 

  • Reynolds, R. G. (1994). An introduction to cultural algorithms. In A. V. Sebald & L. J. Fogel (Eds.) The 3rd Annual Conference on Evolutionary Programming (pp. 131–139). World Scientific Publishing.

    Google Scholar 

  • Reynolds, R. G. (1999). Cultural algorithms: theory and application In D. Corne, M. Dorigo & Glover, F. (Eds.), New Ideas in Optimization. NY: McGraw-Hill.

    Google Scholar 

  • Riff, M. C., Montero, E., & Neveu, B. (2013). Reducing calibration effort for clonal selection based algorithms. Knowledge-Based Systems, 41, 54–67.

    Google Scholar 

  • Rose, S. V. (2008). Volcano and earthquake. New York: Dorling Kindersley Limited. ISBN 978-0-7566-3780-4.

    Google Scholar 

  • Sacco, W. F., Oliveira, C. R. E. D., & Pereira, C. M. N. A. (2006). Two stochastic optimization algorithms applied to nuclear reactor core design. Progress in Nuclear Energy, 48, 525–539.

    Google Scholar 

  • Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2012). Mine blast algorithm for optimization of truss structures with discrete variables. Computers and Structures, 102–103, 49–63.

    Google Scholar 

  • Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing, 13, 2592–2612.

    Google Scholar 

  • Samuels, P., Huntington, S., Allsop, W., & Harrop, J. (2009). Flood risk management: Research and practice. London: Taylor & Francis Group. ISBN 978-0-415-48507-4.

    Google Scholar 

  • Sand, H., Wikenros, C., Wabakken, P., & Liberg, O. (2006). Effects of hunting group size, snow depth and age on the success of wolves hunting moose. Animal Behaviour, 72, 781–789.

    Google Scholar 

  • Savage, N. (2012). Gaining wisdom from crowds. Communications of the ACM, 55, 13–15.

    Google Scholar 

  • Schnell, R. J., & Priyadarshan, P. M. (2012). Genomics of tree crops. New York: Springer. ISBN 978-1-4614-0919-9.

    Google Scholar 

  • Schutter, G. D., Theraulaz, G., & Deneubourg, J.-L. (2001). Animal–robots collective intelligence. Annals of Mathematics and Artificial Intelligence, 31, 223–238.

    Google Scholar 

  • Sedwards, S. (2009). A natural computation approach to biology: Modelling cellular processes and populations of cells with stochastic models of P systems. Unpublished Doctoral Thesis, University of Trento.

    Google Scholar 

  • Sell, S. (2013). Stem cells handbook. New York: Springer. ISBN 978-1-4614-7695-5.

    Google Scholar 

  • Şen, Z. (2014). Philosophical, logical and scientific perspectives in engineering. Heidelberg: Springer. ISBN 978-3-319-01741-9.

    Google Scholar 

  • Senkerik, R., Zelinka, I., Davendra, D., & Oplatkova, Z. (2010). Utilization of soma and differential evolution for robust stabilization of chaotic logistic equation. Computers and Mathematics with Applications, 60, 1026–1037.

    MATH  MathSciNet  Google Scholar 

  • Shann, M. (2008). Emergent behavior in a simulated robot inspired by the slime mold. Unpublished Bachelor Thesis, University of Zurich.

    Google Scholar 

  • Shaw, B., Banerjee, A., Ghoshal, S. P., & Mukherjee, V. (2011). Comparative seeker and bio-inspired fuzzy logic controllers for power system stabilizers. Electrical Power and Energy Systems, 33, 1728–1738.

    Google Scholar 

  • Shettleworth, S. J. (2010). Cognition, evolution, and behavior. New York: Oxford University Press. ISBN 978-0-19-531984-2.

    Google Scholar 

  • Shi, Y. (2011a). Brain storm optimization algorithm. In Y. Tan, Y. Shi & G. Wang (Eds.), ICSI 2011, Pat I, LNCS (Vol. 6728, pp. 303–309). Berlin: Springer.

    Google Scholar 

  • Shi, Y. (2011b). An optimization algorithm based on brainstorming process. International Journal of Swarm Intelligence Research, 2, 35–62.

    Google Scholar 

  • Shlesinger, M. F., Klafter, J., & Zumofen, G. (1999). Above, below and beyond Brownian motion. American Journal of Physics, 67, 1253–1259.

    Google Scholar 

  • Silva, D. J. A. D., Teixeira, O. N., & Oliveira, R. C. L. D. (2012). Performance study of cultural algorithm based on genetic algorithm with single and multi population for the MKP. In S. Gao (Ed.), Bio-inspired computational algorithms and their applications. Rijeka: InTech.

    Google Scholar 

  • Sizer, F. S., & Whitney, E. (2014). Nutrition: Concepts and controversies. Belmont: Cengage Learning. ISBN 978-1-133-60318-4.

    Google Scholar 

  • Smolin, L. A., & Grosvenor, M. B. (2010). Healthy eathing_a guide to nutrition: Nutrition for sports and exercise. New York: Infobase Publishing. ISBN 978-1-60413-804-7.

    Google Scholar 

  • Song, M. X., Chen, K., He, Z. Y., & Zhang, X. (2013). Bionic optimization for micro-siting of wind farm on complex terrain. Renewable Energy, 50, 551–557.

    Google Scholar 

  • Srinivasan, S., & Ramakrishnan, S. (2012). Nugget discovery with a multi-objective cultural algorithm. Computer Science and Engineering: An International Journal, 2, 11–25.

    Google Scholar 

  • Steinbuch, R. (2011). Bionic optimisation of the earthquake resistance of high buildings by tuned mass dampers. Journal of Bionic Engineering, 8, 335–344.

    Google Scholar 

  • Steinitz, M. (2014). Human monoclonal antibodies: Methods and protocols. New York: Springer. ISBN 978-1-62703-585-9.

    Google Scholar 

  • Stradner, J., Thenius, R., Zahadat, P., Hamann, H., Crailsheim, K., & Schmickl, T. (2013). Algorithmic requirements for swarm intelligence in differently coupled collective systems. Chaos: Solitons and Fractals. 50.

    Google Scholar 

  • Stukas, A. A., & Clary, E. G. (2012). Altruism and helping behavior. In V. S. Ramachandran, (Ed.), Encyclopedia of human behavior (2nd ed.). London: Elsevier, Inc. ISBN 978-0-12-375000-6.

    Google Scholar 

  • Su, M.-C., Su, S.-Y., & Zhao, Y.-X. (2009). A swarm-inspired projection algorithm. Pattern Recognition, 42, 2764–2786.

    MATH  Google Scholar 

  • Subbaiah, K. V., Rao, M. N., & Rao, K. N. (2009). Scheduling of AGVs and machines in FMS with makespan criteria using sheep flock heredity algorithm. International Journal of Physical Sciences, 4, 139–148.

    Google Scholar 

  • Sueur, C., Deneubourg, J.-L., & Petit, O. (2010). Sequence of quorums during collective decision making in macaques. Behavioral Ecology and Sociobiology, 64, 1875–1885.

    Google Scholar 

  • Sulaiman, M. H. (2013, March 15–17). Differential search algorithm for economic dispatch with valve-point effects. In 2nd International Conference on Engineering and Applied Science (ICEAS) (pp. 111–117). Tokyo: Toshi Center Hotel.

    Google Scholar 

  • Sulis, W. (1997). Fundamental concepts of collective intelligence. Nonlinear Dynamics, Psychology, and Life Sciences, 1, 35–53.

    MATH  Google Scholar 

  • Sun, J., & Lei, X. (2009). Geese-inspired hybrid particle swarm optimization algorithm. In International Conference on Artificial Intelligence and Computational Intelligence (pp. 134–138). IEEE.

    Google Scholar 

  • Taffe, M. A., & Taffe, W. J. (2011). Rhesus monkeys employ a procedural strategy to reduce working memory load in a self-ordered spatial search task. Brain Research, 1413, 43–50.

    Google Scholar 

  • Taherdangkoo, M., Shirzadi, M. H., & Bagheri, M. H. (2012a). A novel meta-heuristic algorithm for numerical function optimization_blind, naked mole-rats (BNMR) algorithm. Scientific Research and Essays, 7, 3566–3583.

    Google Scholar 

  • Taherdangkoo, M., Yazdi, M., & Bagheri, M. H. (2011). Stem cells optimization algorithm. LNBI (Vol. 6840, pp. 394–403). Berlin: Springer.

    Google Scholar 

  • Taherdangkoo, M., Yazdi, M., & Bagheri, M. H. (2012b). A powerful and efficient evolutionary optimization algorithm based on stem cells algorithm for data clustering. Central European Journal of Computer Science, 2, 1–13.

    Google Scholar 

  • Tan, Y., & Zhu, Y. (2010). Fireworks algorithm for optimization. In Y. Tan, Y. Shi & K. C. Tan (Eds.), ICSI 2010, Part I, LNCS (Vol. 6145, pp. 355–364). Berlin: Springer

    Google Scholar 

  • Taylor, K. (2012). The brain supremacy: Notes from the frontiers of neuroscience. Oxford: Oxford University Press. ISBN 978-0-19-960337-4.

    Google Scholar 

  • Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D. P., Fricker, M. D., et al. (2010). Rules for biologically inspired adaptive network design. Science, 237, 439–442.

    MathSciNet  Google Scholar 

  • Thammano, A., & Moolwong, J. (2010). A new computational intelligence technique based on human group formation. Expert Systems with Applications, 37, 1628–1634.

    Google Scholar 

  • Theiner, G., Allen, C., & Goldstone, R. L. (2010). Recognizing group cognition. Cognitive Systems Research, 11, 378–395.

    Google Scholar 

  • Tidball, K. G., & Krasny, M. E. (2014). Greening in the red zone: Disaster, resilience and community greening. Heidelberg: Springer. ISBN 978-90-481-9946-4.

    Google Scholar 

  • Tollefsen, D. P. (2006). From extended mind to collective mind. Cognitive Systems Research, 7, 140–150.

    Google Scholar 

  • Touhara, K. (2013). Pheromone signaling: Methods and protocols. New York: Springer. ISBN 978-1-62703-618-4.

    Google Scholar 

  • Ulutas, B. H., & Kulturel-Konak, S. (2011). A review of clonal selection algorithm and its applications. Artificial Intelligence Review, 36, 117–138.

    Google Scholar 

  • Umedachi, T., Takeda, K., Nakagaki, T., Kobayashi, R., & Ishiguro, A. (2010). Fully decentralized control of a soft-bodied robot inspired by true slime mold. Biological Cybernetics, 102, 261–269.

    Google Scholar 

  • Venkumar, P., & Sekar, K. C. (2012). Design of cellular manufacturing system using non-traditional optimization algorithms. In V. Modrák & R. S. Pandian (Eds.), Operations management research and cellular manufacturing systems: Innovative methods and approaches, Chap. 6 (pp. 99–139). Hershey: IGI Global.

    Google Scholar 

  • Verdy, A., & Flierl, G. (2008). Evolution and social behavior in krill. Deep-Sea Research II, 55, 472–484.

    Google Scholar 

  • Vucetich, J. A., Peterson, R. O., & Waite, T. A. (2004). Raven scavenging favours group foraging in wolves. Animal Behaviour, 67, 1117–1126.

    Google Scholar 

  • Wang, G., Guo, L., Gandomi, A. H., Cao, L., Alavi, A. H., Duan, H., et al. (2013). Lévy-flight krill herd algorithm. Mathematical Problems in Engineering, 2013, 1–14.

    Google Scholar 

  • Wang, P., & Cheng, Y. (2010). Relief supplies scheduling based on bean optimization algorithm. Economic Research Guide, 8, 252–253.

    Google Scholar 

  • Wang, S., Dai, D., Hu, H., Chen, Y.-L., & Wu, X. (2011). RBF neural network parameters optimization based on paddy field algorithm. In International Conference on Information and Automation (ICIA) (pp. 349–353). June, Shenzhen, China. IEEE.

    Google Scholar 

  • Wang, W., Feng, Q., & Zheng, Y. (2008, November 19–21). A novel particle swarm optimization algorithm with stochastic focusing search for real-parameter optimization. In 11th Singapore International Conference on Communication Systems (ICCS) (pp. 583–587). Guangzhou, China. IEEE.

    Google Scholar 

  • Wang, X., Gao, X.-Z., & Ovaska, S. J. (2009). Fusion of clonal selection algorithm and harmony search method in optimization of fuzzy classification systems. International Journal of Bio-Inspired Computation, 1, 80–88.

    Google Scholar 

  • Wang, Z.-R., Ma, F., Ju, T., & Liu, C.-M. (2010). A niche genetic algorithm with population migration strategy. In IEEE 2nd International Conference on Information Science and Engineering (ICISE) (pp. 912–915).

    Google Scholar 

  • Wei, G. (2011). Optimization of mine ventilation system based on bionics algorithm. Procedia Engineering, 26, 1614–1619.

    Google Scholar 

  • Wei, Z. H., Cui, Z. H., & Zeng, J. C. (2010, September 26–28). Social cognitive optimization algorithm with reactive power optimization of power system. In 2010 International Conference on Computational Aspects of Social Networks (CASoN) (pp. 11–14). Taiyuan, China.

    Google Scholar 

  • Weigert, G., Horn, S., & Werner, S. (2006). Optimization of manufacturing processes by distributed simulation. International Journal of Production Research, 44, 3677–3692.

    MATH  Google Scholar 

  • Whitehouse, M. E. A., & Lubin, Y. (1999). Competitive foraging in the social spider Stegodyphus dumicola. Animal Behaviour, 58, 677–688.

    Google Scholar 

  • Whitten, K. W., Davis, R. E., Peck, M. L., & Stanley, G. G. (2014). Chemistry. Belmont: Cengage Learning. ISBN 13: 978-1-133-61066-3.

    Google Scholar 

  • Wilson, C. (2013). Brainstroming and beyond: a user-centered design method. Waltham: Morgan Kaufmann, Elsevier Inc. ISBN 978-0-12-407157-5.

    Google Scholar 

  • Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1, 67–82.

    Google Scholar 

  • Woodward, J. (2008). Climate change. New York: Dorling Kindersley Limited. ISBN 978-07566-3771-2.

    Google Scholar 

  • Woodworth, S. (2007). Computability limits in membrane computing. Unpublished Doctoral Thesis, University of California, Santa Barbara.

    Google Scholar 

  • Wu, J., Cui, Z., & Liu, J. (2011, August 18–20). Using hybrid social emotional optimization algorithm with metropolis rule to solve nonlinear equations. In Y. Wang, A. Celikyilmaz, W. Kinsner, W. Pedrycz, H. Leung & L. A. Zadeh (Eds.), 10th International Conference on Cognitive Informatics and Cognitive Computing (ICCI & CC) (pp. 405–411). Banff, AB. IEEE.

    Google Scholar 

  • Xiao, J.-H., Huang, Y.-F., & Cheng, Z. (2013). A bio-inspired algorithm based on membrane computing for engineering design problem. International Journal of Computer Science Issues, 10, 580–588.

    Google Scholar 

  • Xu, Y. C., Cui, Z. H., & Zeng, J. C. (2010). Social emotional optimization algorithm for nonlinear constrained optimization problems. In 1st International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO) (pp. 583–590).

    Google Scholar 

  • Xue, J., Wu, Y., Shi, Y., & Cheng, S. (2012). Brain storm optimization algorithm for multi-objective optimization problems. In Y. Tan, Y. Shi & Z. Ji (Eds.), ICSI 2012, Part I, LNCS (Vol. 7331, pp. 513–519). Berlin: Springer.

    Google Scholar 

  • Yampolskiy, R. V., Ashby, L., & Hassan, L. (2012). Wisdom of artificial crowds: A metaheuristic algorithm for optimization. Journal of Intelligent Learning Systems and Applications, 4, 98–107.

    Google Scholar 

  • Yang, C., Tu, X., & Chen, J. (2007). Algorithm of marriage in honey bees optimization based on the wolf pack search. In IEEE International Conference on Intelligent Pervasive Computing (IPC) (pp. 462–467).

    Google Scholar 

  • Yang, X.-S. (2005). Biology-derived algorithms in engineering optimization. In S. Olarius & A. Zomaya (Eds.), Handbook of Bioinspired Algorithms and Applications, Chap. 32 (pp. 585–596). Boca Raton: CRC Press.

    Google Scholar 

  • Yang, X.-S. (2012). Flower pollination algorithm for global optimization. Unconventional Computation and Natural Computation, LNCS (Vol. 7445, pp. 240–249). Berlin: Springer.

    Google Scholar 

  • Yang, X.-S., & Deb, S. (2010). Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. In J. R. Gonzalez (Ed.), Nature Inspired Cooperative Strategies for Optimization (NISCO 2010), SCI (Vol. 284, pp. 101–111). Berlin: Springer.

    Google Scholar 

  • Yang, X.-S., & Deb, S. (2012). Two-stage eagle strategy with differential evolution. International Journal of Bio-Inspired Computation, 4, 1–5.

    Google Scholar 

  • Yang, X.-S., Karamanoglu, M., & He, X. (2013). Multi-objective flower algorithm for optimization. Procedia Computer Science, 18, 861–868.

    Google Scholar 

  • Yeagle, P. L. (Ed.). (2005). The structure of biological membranes. Boca Raton: CRC Press. ISBN 0-8493-1403-8.

    Google Scholar 

  • You, S. K., Kwon, D. H., Park, Y.-I., Kim, S. M., Chung, M.-H., & Kim, C. K. (2009). Collective behaviors of two-component swarms. Journal of Theoretical Biology, 261, 494–500.

    MathSciNet  Google Scholar 

  • Zaharie, D., & Ciobanu, G. (2006). Distributed evolutionary algorithms inspired by membranes in solving continuous optimization problems. In H. J. Hoogeboom (Ed.), WMC 7, LNCS (Vol. 4361, pp. 536–553). Berlin: Springer.

    Google Scholar 

  • Zang, H., Zhang, S., & Hapeshi, K. (2010). A review of nature-inspired algorithms. Journal of Bionic Engineering, 7, S232–S237.

    Google Scholar 

  • Zelinka, I., & Lampinen, J. (2000). Soma: Self-organizing migrating algorithm. In The 6th International Conference on Soft Computing, Brno, Czech Republic.

    Google Scholar 

  • Zelinka, I., Senkerik, R., & Navratil, E. (2009). Investigation on evolutionary optimization of chaos control. Chaos, Solitons and Fractals, 40, 111–129.

    MATH  Google Scholar 

  • Zhan, Z.-H., Zhang, J., Shi, Y.-H., & Liu, H.-L. (2012, June 10–15) A modified brain storm optimization. In World Congress on Computational Intelligence (WCCI) (pp. 1–8). Brisbane, Australia. IEEE.

    Google Scholar 

  • Zhang, G., Cheng, J., & Gheorghe, M. (2011). A membrane-inspired approximate algorithm for traveling salesman problems. Romanian Journal of Information Science and Technology, 14, 3–19.

    Google Scholar 

  • Zhang, G., Yang, H., & Liu, Z. (2007). Using watering algorithm to find the optimal paths of a maze. Computer, 24, 171–173.

    Google Scholar 

  • Zhang, W., Luo, Q. & Zhou, Y. (2009). A method for training RBF neural networks based on population migration algorithm. In International Conference on Artificial Intelligence and Computational Intelligence (AICI) (pp. 165–169). IEEE.

    Google Scholar 

  • Zhang, W., & Zhou, Y. (2009). Description population migration algorithm based on framework of swarm intelligence. In IEEE WASE International Conference on Information Engineering (ICIE) (pp. 281–284).

    Google Scholar 

  • Zhang, X., Chen, W., & Dai, C. (2008a, April 6–9) Application of oriented search algorithm in reactive power optimization of power system. DRPT 2008 (pp. 2856–2861). Nanjing, China. DRPT.

    Google Scholar 

  • Zhang, X., Sun, B., Mei, T., & Wang, R. (2010, November 28–30). Post-disaster restoration based on fuzzy preference relation and bean optimization algorithm. In Youth Conference on Information Computing and Telecommunications (YC-ICT) (pp. 271–274). IEEE.

    Google Scholar 

  • Zhang, X., Jiang, K., Wang, H., Li, W., & Sun, B. (2012a). An improved bean optimization algorithm for solving TSP. In Y. Tan, Y. Shi & Z. Ji (Eds.), ICSI 2012, Part I, LNCS (Vol. 7331, pp. 261–267). Berlin: Springer.

    Google Scholar 

  • Zhang, X., Huang, S., Hu, Y., Zhang, Y., Mahadevan, S., & Deng, Y. (2013a). Solving 0-1 knapsack problems based on amoeboid organism algorithm. Applied Mathematics and Computation, 219, 9959–9970.

    MathSciNet  Google Scholar 

  • Zhang, X., Sun, B., Mei, T., & Wang, R. (2013b). A novel evolutionary algorithm inspired by beans dispersal. International Journal of Computational Intelligence Systems, 6, 79–86.

    Google Scholar 

  • Zhang, X., Wang, H., Sun, B., Li, W., & Wang, R. (2013c). The Markov model of bean optimization algorithm and its convergence analysis. International Journal of Computational Intelligence Systems, 6, 609–615.

    Google Scholar 

  • Zhang, X., Wang, R., & Song, L. (2008b). A novel evolutionary algorithm: Seed optimization algorithm. Pattern Recognition and Artificial Intelligence, 21, 677–681.

    Google Scholar 

  • Zhang, Z.-W., Zhang, H., & Li, Y.-B. (2012b). Biologically inspired collective construction with visual landmarks. Journal of Zhejiang University-SCIENCE C (Computers and Electronics), 13, 315–327.

    Google Scholar 

  • Zhao, Q., & Liu, X. (2011). An improved multi-objective population migration optimization algorithm. In 2nd International Symposium on Intelligence Information Processing and Trusted Computing (IPTC) (pp. 143–146). IEEE.

    Google Scholar 

  • Zheng, Y., Chen, W., Dai, C., & Wang, W. (2009). Stochastic focusing search: A novel optimization algorithm for real-parameter optimization. Journal of Systems Engineering and Electronics, 20, 869–876.

    Google Scholar 

  • Zhou, D., Shi, Y., & Cheng, S. (2012). Brain storm optimization algorithm with modified step-size and individual generation. In Y. Tan, Y. Shi & Z. Ji (Eds.), ICSI 2012, Part I, LNCS (Vol. 7331, pp. 243–252). Berlin: Springer.

    Google Scholar 

  • Zhou, Y., & Liu, B. (2009). Two novel swarm intelligence clustering analysis methods. In IEEE Fifth International Conference on Natural Computation (ICNC) (pp. 497–501).

    Google Scholar 

  • Zhou, Y., & Mao, Z. (2003). A new search algorithm for global optimization: Population migration algorithm. Journal of South China University of Technology, A31, 1–5.

    Google Scholar 

  • Zungeru, A. M., Ang, L.-M., & Seng, K. P. (2012). Termite-hill: Performance optimized swarm intelligence based routing algorithm for wireless sensor networks. Journal of Network and Computer Applications, 35, 1901–1917.

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, Built Environment and Information Technology, Department of Mechanical Engineering and Aeronautical Engineering, University of Pretoria, Pretoria, South Africa

    Bo Xing

  2. Department of New Product Development, Meiyuan Mould Design and Manufacturing Co., Ltd., Xianghe, People’s Republic of China

    Wen-Jing Gao

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  1. Bo Xing
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Xing, B., Gao, WJ. (2014). Emerging Biology-based CI Algorithms. In: Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms. Intelligent Systems Reference Library, vol 62. Springer, Cham. https://doi.org/10.1007/978-3-319-03404-1_17

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