Introduction to Intelligent Search Algorithms

  • Bo Xing
  • Tshilidzi Marwala
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 129)


This chapter introduces some general knowledge relative to the broad area of intelligent search algorithms. The desirable merits of these clever algorithms and their remarkable achievements in many fields have inspired researchers (from a variety of disciplines) to continuously develop their ameliorated versions. Some historical information regarding search and artificial intelligence are briefed in Sect. 3.1. Then, the relevant developed-, developing-, and emerging-intelligent search algorithms are presented in Sect. 3.2. Section 3.3 summarises this chapter.


  1. Abbass, H. A. (2001). MBO: Marriage in honey bees optimization. A Haplometrosis Polygynous swarming approach. Paper presented at the 2001 Congress on Evolutionary Computation (CEC), May 27–30, Seoul, South Korea, pp. 207–214.Google Scholar
  2. Abdechiri, M., Meybodi, M. R., & Bahrami, H. (in press). Gases Brownian motion optimization: An algorithm for optimization (GBMO). Applied Soft Computing.
  3. 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.MathSciNetzbMATHCrossRefGoogle Scholar
  4. Akbari, R., Mohammadi, A., & Ziarati, K. (2009). A powerful bee swarm optimization algorithm. Paper presented at the IEEE 13th International Multitopic Conference (INMIC), pp. 1–6.Google Scholar
  5. Alatas, B. (2011). ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Systems with Applications, 38, 13170–13180.CrossRefGoogle Scholar
  6. Alsmadi, M. K. (2017). An efficient similarity measure for content based image retrieval using memetic algorithm. Egyptian Journal of Basic and Applied Sciences, 4, 112–122.CrossRefGoogle Scholar
  7. Anandaraman, C., Sankar, A. V. M., & Natarajan, R. (2012). A new evolutionary algorithm based on bacterial evolution and its applications for scheduling a flexible manufacturing system. Jurnal Teknik Industri, 14(1), 1–12.CrossRefGoogle Scholar
  8. 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 7331 (pp. 233–242). Berlin, Heidelberg: Springer.Google Scholar
  9. Ashby, L. H., & Yampolskiy, R. V. (2011). Genetic algorithm and wisdom of artificial crowds algorithm applied to light up. Paper presented at the 16th International Conference on Computer Games (CGAMES 2011), pp. 27–32.Google Scholar
  10. Ashrafi, S. M., & Dariane, A. B. (2011). A novel and effective algorithm for numerical optimization: melody search (MS). Paper presented at the 11th International Conference on Hybrid Intelligent Systems (HIS), December 5–8, Malacca, pp. 109–114.Google Scholar
  11. Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures, 169, 1–12.CrossRefGoogle Scholar
  12. Atashpaz-Gargari, E., & Lucas, C. (2007). Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. Paper presented at the IEEE Congress on Evolutionary Computation (CEC 2007), pp. 4661–4667.Google Scholar
  13. Bakhshipour, M., Ghadi, M. J., & Namdari, F. (2017). Swarm robotics search & rescue: A novel artificial intelligence-inspired optimization approach. Applied Soft Computing, 57, 708–726.CrossRefGoogle Scholar
  14. Barzegar, B., Rahmani, A. M., & Zamanifar, K. (2009). Gravitational emulation local search algorithm for advanced reservation and scheduling in grid systems. Paper presented at the First Asian Himalayas International Conference on Internet (AH-ICI), pp. 1–5.Google Scholar
  15. Bastos-Filho, C. J. A., Lima-Neto, F. B. d., Lins, A. J. C. C., Nascimento, A. I. S., & Lima, M. P. (2008). A novel search algorithm based on fish school behavior. Paper presented at the IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 2646–2651.Google Scholar
  16. Basu, S., Chaudhuri, C., Kundu, M., Nasipuri, M., & Basu, D. K. (2007). Text line extraction from multi-skewed handwritten documents. Pattern Recognition, 40(6), 1825–1839.zbMATHCrossRefGoogle Scholar
  17. Baykasoğlu, A., & Akpinar, Ş. (2015). Weighted superposition attraction (WSA): A swarm intelligence algorithm for optimization problems—Part 2: Constrained optimization. Applied Soft Computing, 37, 396–415.CrossRefGoogle Scholar
  18. Bayraktar, Z., Komurcu, M., & Werner, D. H. (2010). Wind driven optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics. Paper presented at the Proceedings of IEEE International Symposium on Antennas and Propagation Society, July 2010, pp. 1–4.Google Scholar
  19. 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 7332 (pp. 117–126). Berlin, Heidelberg: Springer.Google Scholar
  20. Bezdek, J. C. (1992). On the relationship between neural networks, pattern recognition and intelligence. International Journal of Approximate Reasoning, 6, 85–107.CrossRefGoogle Scholar
  21. Bezdek, J. C. (1994). What is computational intelligence? In J. M. Zurada, R. J. Marks, & C. J. Robinson (Eds.), Computational intelligence imitating life (pp. 1–12). Los Alamitos: IEEE Press.Google Scholar
  22. Bharathi, M. A., Vijayakumar, B. P., & Manjaiah, D. H. (2013). Cluster based data aggregation in WSN using swarm optimization technique. International Journal of Engineering and Innovative Technology (IJEIT), 2(12), 140–144.Google Scholar
  23. Birbil, Şİ., & Fang, S.-C. (2003). An electromagnetism-like mechanism for global optimization. Journal of Global Optimization, 25, 263–282.MathSciNetzbMATHCrossRefGoogle Scholar
  24. Bitam, S., & Mellouk, A. (2013). Bee life-based multi constraints multicast routing optimization for vehicular ad hoc networks. Journal of Network and Computer Applications, 36, 981–991.CrossRefGoogle Scholar
  25. Boettcher, S., & Percus, A. (2000). Nature’s way of optimizing. Artificial Intelligence, 119, 275–286.zbMATHCrossRefGoogle Scholar
  26. Cai, W., Yang, W., & Chen, X. (2008). A global optimization algorithm based on plant growth theory: plant growth optimization. Paper presented at the Proceedings of the International Conference on Intelligent Computation Technology and Automation, Vol. 1, pp. 1194–1199.Google Scholar
  27. Castro, L. N. d., & Zuben, F. J. V. (2000). The clonal selection algorithm with engineering applications. Paper presented at the Workshop on Artificial Immune Systems and Their Applications, Las Vegas, USA, July, pp. 1–7.Google Scholar
  28. Chakri, A., Khelif, R., Benouaret, M., & Yang, X.-S. (2017). New directional bat algorithm for continuous optimization problems. Expert Systems with Applications, 69, 159–175.CrossRefGoogle Scholar
  29. Chen, T. (2009). A simulative bionic intelligent optimization algorithm: artificial searching swarm algorithm and its performance analysis. Paper presented at the International Joint Conference on Computational Sciences and Optimization (CSO), pp. 864–866.Google Scholar
  30. Chen, T., Wang, Y., & Li, J. (2012). Artificial tribe algorithm and its performance analysis. Journal of Software, 7(3), 651–656.CrossRefGoogle Scholar
  31. Chen, Z., & Tang, H. (2010). Cockroach swarm optimization. Paper presented at the 2nd International Conference on Computer Engineering and Technology (ICCET), pp. 652–655.Google Scholar
  32. Cheng, M.-Y., & Prayogo, D. (2014). Symbiotic organisms search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98–112.CrossRefGoogle Scholar
  33. Chu, S.-C., & Tsai, P.-W. (2007). Computational intelligence based on the behavior of cats. International Journal of Innovative Computing, Information and Control, 3(1), 163–173.Google Scholar
  34. Chuang, C.-L., & Jiang, J.-A. (2007). Integrated radiation optimization: Inspired by the gravitational radiation in the curvature of space-time. Paper presented at the IEEE Congress on Evolutionary Computation (CEC), September 25–28, Singapore, pp. 3157–3164, IEEE.Google Scholar
  35. Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229–247.CrossRefGoogle Scholar
  36. Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219, 8121–8144.MathSciNetzbMATHCrossRefGoogle Scholar
  37. Comellas, F., & Martínez-Navarro, J. (2009). Bumblebees: A multiagent combinatorial optimization algorithm inspired by social insect behaviour. Paper presented at the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation (GEC), pp. 811–814.Google Scholar
  38. Cortés, P., García, J. M., Muñuzuri, J., & Onieva, L. (2008). Viral systems: A new bio-inspired optimisation approach. Computers & Operations Research, 35(9), 2840–2860.zbMATHCrossRefGoogle Scholar
  39. Cuevas, E., Cienfuegos, M., Zaldívar, D., & Pérez-Cisneros, M. (in press). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications.
  40. Cuevas, E., Echavarría, A., Zaldívar, D., & Pérez-Cisneros, M. (2013a). A novel evolutionary algorithm inspired by the states of matter for template matching. Expert Systems with Applications, 40, 6359–6373.CrossRefGoogle Scholar
  41. 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
  42. Cui, X., Gao, J., & Potok, T. E. (2006). A flocking based algorithm for document clustering analysis. Journal of Systems Architecture, 52, 505–515.CrossRefGoogle Scholar
  43. Cui, Y. H., Guo, R., Rao, R. V., & Savsani, V. J. (2008). Harmony element algorithm—a naive initial searching range. Paper presented at the International Conference on Advances in Mechanical Engineering, December 15–17, S.V. National Institute of Technology, Gujarat, India, pp. 1–6.Google Scholar
  44. Dai, C., Zhu, Y., & Chen, W. (2007). Seeker optimization algorithm. In Y. Wang, Y. Cheung, & H. Liu (Eds.), CIS 2006, LNAI 4456 (pp. 167–176). Berlin, Heidelberg: Springer.Google Scholar
  45. Daskin, A., & Kais, S. (2011). Group leaders optimization algorithm. Molecular Physics, 109(5), 761–772.CrossRefGoogle Scholar
  46. 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.MathSciNetzbMATHCrossRefGoogle Scholar
  47. de Oliveira, D. R., Parpinelli, R. S., & Lopes, H. S. (2011). Bioluminescent swarm optimization algorithm. Evolutionary algorithms (Chapter 5, pp. 71–84). Eisuke Kita: InTech.Google Scholar
  48. Deb, S., Fong, S., & Tian, Z. (2015). Elephant search algorithm for optimization problems. Paper presented at the Proceedings of the 10th International Conference on Digital Information Management (ICDIM), pp. 249–255.Google Scholar
  49. del Acebo, E., & de la Rosa, J. L. (2008). Introducing bar systems: A class of swarm intelligence optimization algorithms. Paper presented at the AISB 2008 Symposium on Swarm Intelligence Algorithms and Applications, April 1–4, University of Aberdeen, pp. 18–23.Google Scholar
  50. Dhar, J., & Arora, S. (in press). Designing fuzzy rule base using spider monkey optimization algorithm in cooperative framework. Future Computing and Informatics Journal.
  51. Doğan, B., & Ölmez, T. (2015). A new metaheuristic for numerical function optimization: Vortex search algorithm. Information Sciences, 293, 125–145.CrossRefGoogle Scholar
  52. Du, W., Wang, P., Song, L., & Cheng, L. (2015). Optimization of volume to point conduction problem based on a novel thermal conductivity discretization algorithm. Chinese Journal of Chemical Engineering, 23, 1161–1168.CrossRefGoogle Scholar
  53. Duch, W. (2007). What is computational intelligence and where is it going? In W. Duch & J. Mańdziuk (Eds.), Challenges for computational intelligence (pp. 1–13). Berlin, Heidelberg: Springer. Chapter 1. ISBN 978-3-540-71983-0.CrossRefGoogle Scholar
  54. Dueck, G. (1993). New optimization heuristics: The great deluge algorithm and the record-to-record travel. Journal of Computational Physics, 104, 86–92.zbMATHCrossRefGoogle Scholar
  55. 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.MathSciNetCrossRefGoogle Scholar
  56. Ebrahimi, A., & Khamehchi, E. (2016). Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems. Journal of Natural Gas Science and Engineering, 29, 211–222.CrossRefGoogle Scholar
  57. Eesa, A. S., Orman, Z., & Brifcani, A. M. A. (2015). A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Expert Systems with Applications, 42, 2670–2679.CrossRefGoogle Scholar
  58. Ehteram, M., Karami, H., Mousavi, S.-F., El-Shafie, A., & Amini, Z. (2017). Optimizing dam and reservoirs operation based model utilizing shark algorithm approach. Knowledge-Based Systems, 122, 26–38.CrossRefGoogle Scholar
  59. Erol, O. K., & Eksin, I. (2006). A new optimization method: Big bang-big crunch. Advances in Engineering Software, 37, 106–111.CrossRefGoogle Scholar
  60. Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm—A novel metaheuristic optimization for solving constrained engineering optimization problems. Computers & Structures, 110–111, 151–166.CrossRefGoogle Scholar
  61. Eusuff, M. M., & Lansey, K. E. (2003). Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management, 129(3), 210–225.CrossRefGoogle Scholar
  62. 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 5 (pp. 1310–1321). Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.Google Scholar
  63. Flasiński, M. (2016). Introduction to artificial intelligence. Switzerland: Springer International Publishing. ISBN 978–3-319-40020-4.Google Scholar
  64. Flores, J. J., López, R., & Barrera, J. (2011). Gravitational interactions optimization. Learning and intelligent optimization (pp. 226–237). Berlin, Heidelberg: Springer.Google Scholar
  65. Formato, R. A. (2007). Central force optimization: A new metaheuristic with applications in applied electromagnetics. Progress in Electromagnetics Research, PIER, 77, 425–491.CrossRefGoogle Scholar
  66. Frost, J. R., & Stone, L. D. (2001). Review of search theory: Advances and applications to search and rescue decision support. USA: U.S. Coast Guard Research and Development Center, No. CG-D-15-01.Google Scholar
  67. Gandomi, A. H. (2014). Interior search algorithm (ISA): A novel approach for global optimization. ISA Transactions, 53, 1168–1183.Google Scholar
  68. Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17, 4831–4845.MathSciNetzbMATHCrossRefGoogle Scholar
  69. Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60–68.CrossRefGoogle Scholar
  70. Ghaemi, M., & Feizi-Derakhshi, M.-R. (2014). Forest optimization algorithm. Expert Systems with Applications, 41, 6676–6687.CrossRefGoogle Scholar
  71. Häckel, S., & Dippold, P. (2009). The bee colony-inspired algorithm (BCiA)—A two-stage approach for solving the vehicle routing problem with time windows. Paper presented at the GECCO’09, July 8–12, Nontréal, Québec, Canada, pp. 25–32.Google Scholar
  72. Hasançebi, O., & Azad, S. K. (2012). An efficient metaheuristic algorithm for engineering optimization: SPOT. International Journal of Optimization in Civil Engineering, 2(4), 479–487.Google Scholar
  73. Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Information Sciences, 2322, 175–184.MathSciNetCrossRefGoogle Scholar
  74. Havens, T. C., Spain, C. J., Salmon, N. G., & Keller, J. M. (2008). Roach infestation optimization. Paper presented at the IEEE Swarm Intelligence Symposium, September 21–23, St. Louis, MO, USA, pp. 1–7.Google Scholar
  75. He, S., Wu, Q. H., & Saunders, J. R. (2006). A novel group search optimizer inspired by animal behavioural ecology. Paper presented at the IEEE Congress on Evolutionary Computation (CEC), July 16–21, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, pp. 1272–1278.Google Scholar
  76. Hersovici, M., Jacovi, M., Maarek, Y. S., Pelleg, D., Shtalhaim, M., & Ur, S. (1998). The shark-search algorithm. An application: Tailored Web site mapping. Computer Networks and ISDN Systems, 30, 317–326.CrossRefGoogle Scholar
  77. Hsiao, Y.-T., Chuang, C.-L., Jiang, J.-A., & Chien, C.-C. (2005). A novel optimization algorithm: space gravitational optimization. Paper presented at the IEEE International Conference on Systems, Man and Cybernetics (SMC), October 10–12, pp. 2323–2328.Google Scholar
  78. Irizarry, R. (2005). A generalized framework for solving dynamic optimization problems using the artificial chemical process paradigm: Applications to particulate processes and discrete dynamic systems. Chemical Engineering Science, 60, 5663–5681.CrossRefGoogle Scholar
  79. Jaddi, N. S., Alvankarian, J., & Abdullah, S. (2017). Kidney-inspired algorithm for optimization problems. Communication on Nonlinear Science and Numerical Simulation, 42, 358–369.CrossRefGoogle Scholar
  80. Jafari-Marandi, R., & Smith, B. K. (2017). Fluid Genetic Algorithm (FGA). Journal of Computational Design and Engineering, 4, 158–167.CrossRefGoogle Scholar
  81. Jin, G.-G., & Tran, T.-D. (2010). A nature-inspired evolutionary algorithm based on spiral movements. Paper presented at the SICE Annual Conference, August 18–21, The Grand Hotel, Taipei, Taiwan, pp. 1643–1647.Google Scholar
  82. Junior, L. S., & Nedjah, N. (2017). Wave algorithm applied to collective navigation of robotic swarms. Applied Soft Computing, 57, 698–707.CrossRefGoogle Scholar
  83. Kaboli, S. H. A., Selvaraj, J., & Rahim, N. A. (2017). Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems. Journal of Computational Science, 19, 31–42.CrossRefGoogle Scholar
  84. Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39, 459–471.MathSciNetzbMATHCrossRefGoogle Scholar
  85. Karci, A., & Alatas, B. (2006). Thinking capability of saplings growing up algorithm. Intelligent Data Engineering and Automated Learning (IDEAL 2006), LNCS 4224 (pp. 386–393). Berlin, Heidelberg: Springer.Google Scholar
  86. Kashan, A. H. (2009). League championship algorithm: A new algorithm for numerical function optimization. Paper presented at the International Conference of Soft Computing and Pattern Recognition (SoCPAR), pp. 43–48.Google Scholar
  87. Kashan, A. H. (2015). A new metaheuristic for optimization: optics inspired optimization (OIO). Computers & Operations Research, 55, 99–125.MathSciNetzbMATHCrossRefGoogle Scholar
  88. Kaveh, A., & Farhoudi, N. (2013). A new optimization method: Dolphin echolocation. Advances in Engineering Software, 59, 53–70.CrossRefGoogle Scholar
  89. Kaveh, A., & Khayatazad, M. (2012). A new meta-heuristic method: Ray optimization. Computers & Structures, 112–113, 283–294.CrossRefGoogle Scholar
  90. Kaveh, A., & Mahdavi, V. R. (2014). Colliding bodies optimization: A novel meta-heuristic method. Computers & Structures, 139, 18–27.CrossRefGoogle Scholar
  91. Kaveh, A., Share, M. A. M., & Moslehi, M. (2013). Magnetic charged system search: A new meta-heuristic algorithm for optimization. Acta Mechanica, 224, 85–107.zbMATHCrossRefGoogle Scholar
  92. Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: Charged system search. Acta Mechanica, 213(3–4), 267–289.zbMATHCrossRefGoogle Scholar
  93. Krishnanand, K. N., & Ghose, D. (2005). Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. Paper presented at the IEEE Swarm Intelligence Symposium (SIS), pp. 84–91.Google Scholar
  94. Kundu, S. (1999). Gravitational clustering: A new approach based on the spatial distribution of the points. Pattern Recognition, 32, 1149–1160.CrossRefGoogle Scholar
  95. Lam, A. Y. S., & Li, V. O. K. (2010). Chemical-reaction-inspired metaheuristic for optimization. IEEE Transactions on Evolutionary Computation, 14(3), 381–399.CrossRefGoogle Scholar
  96. Lei, X., Gao, Z., Duan, M., & Pan, W. (2015). Method for sphericity error evaluation using geometry optimization searching algorithm. Precision Engineering, 42, 101–112.CrossRefGoogle Scholar
  97. Li, B., & Jiang, W. (1998). Optimizing complex functions by chaos search. Cybernetics and Systems: An International, 29(4), 409–419.zbMATHCrossRefGoogle Scholar
  98. Li, X.-L. (2003). A new intelligent optimization method—artificial fish school algorithm (in Chinese with English abstract). (Unpublished Doctoral Thesis), Zhejiang University, Hangzhou, P.R. China.Google Scholar
  99. Li, X., Zhang, J., & Yin, M. (2014). Animal migration optimization: An optimization algorithm inspired by animal migration behavior. Neural Computing & Application, 24(7–8), 1867–1877.CrossRefGoogle Scholar
  100. Liu, C., Yan, X., Liu, C., & Wu, H. (2011). The wolf colony algorithm and its application. Chinese Journal of Electronics, 20(2), 212–216.Google Scholar
  101. Łukasik, S., & Żak, S. (2009). Firefly algorithm for continuous constrained optimization tasks. Computational collective intelligence. semantic web, social networks and multiagent systems, LNCS 5796 (pp. 97–106). Berlin: Spinger.Google Scholar
  102. Ma, L., Zhu, Y., Liu, Y., Tian, L., & Chen, H. (2015). A novel bionic algorithm inspired by plant root foraging behaviors. Applied Soft Computing, 37, 95–113.CrossRefGoogle Scholar
  103. Maia, R. D., Castro, L. N. d., & Caminhas, W. M. (2012). Bee colonies as model for multimodal continuous optimization: The OptBees algorithm. Paper presented at the IEEE World Congress on Computational Intelligence (WCCI), June 10–15, Brisbane, Australia, pp. 1–8.Google Scholar
  104. Malakooti, B., Sheikh, S., Al-Najjar, C., & Kim, H. (2013). Multi-objective energy aware multiprocessor scheduling using bat intelligence. Journal of Intelligent Manufacturing, 24, 805–819.CrossRefGoogle Scholar
  105. Maniezzo, V., Stützle, T., & Voß, S. (Eds.). (2009). Matheuristics: hybridizing metaheuristics and mathematical programming. New York, Dordrecht, Heidelberg, London: Springer Science + Business Media, LLC. ISBN 978–1-4419-1305-0.Google Scholar
  106. Marinakis, Y., Marinaki, M., & Migdalas, A. (2017). An adaptive bumble bees mating optimization algorithm. Applied Soft Computing, 55, 13–30.CrossRefGoogle Scholar
  107. Marwala, T. (2009). Computational intelligence for missing data imputation, estimation and management: Knowledge optimization techniques. New York, USA: IGI Global. ISBN 978-1-60566-336-4.CrossRefGoogle Scholar
  108. Marwala, T. (2010). Finite-element-model updating using computational intelligence techniques: Applications to structural dynamics. London, UK: Springer. ISBN 978-1-84996-322-0.zbMATHCrossRefGoogle Scholar
  109. Marwala, T. (2012). Condition monitoring using computational intelligence methods: Applications in mechanical and electrical systems. London: Springer. ISBN 978-1-4471-2379-8.CrossRefGoogle Scholar
  110. Marwala, T. (2013). Economic modeling using artificial intelligence methods. London, Heidelberg, New York, Dordrecht: Springer. ISBN 978-1-4471-5009-1.zbMATHCrossRefGoogle Scholar
  111. Marwala, T. (2014). Artificial intelligence techniques for rational decision making. Cham, Heidelberg, New York, Dordrecht, London, Switzerland: Springer International Publishing. ISBN 978-3-319-11423-1.zbMATHCrossRefGoogle Scholar
  112. Marwala, T. (2015). Causality, correlation and artificial intelligence for rational decision making. Singapore: World Scientific Publishing Co. Pte. Ltd. ISBN 978-9-81463-086-3.Google Scholar
  113. Marwala, T., Boulkaibet, I., & Adhikari, S. (2017). Probabilistic finite element model updating using Bayesian statistics: Applications to aeronautical and mechanical engineering. United Kingdom: Wiley. ISBN 978-1-1191-5301-6.Google Scholar
  114. Marwala, T., & Lagazio, M. (2011). Militarized conflict modeling using computational intelligence. London, UK: Springer. ISBN 978-0-85729-789-1.CrossRefGoogle Scholar
  115. McCaffrey, J. D., & Dierking, H. (2009). An empirical study of unsupervised rule set extraction of clustered categorical data using a simulated bee colony algorithm. In G. Governatori, J. Hall, & A. Paschke (Eds.), RuleML 2009, LNCS 5858 (pp. 182–193). Berlin, Heidelberg: Springer.Google Scholar
  116. Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1, 355–366.CrossRefGoogle Scholar
  117. Melin, P., Astudillo, L., Castillo, O., Valdez, F., & Valdez, F. (2013). Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm. Expert Systems with Applications, 40, 3185–3195.CrossRefGoogle Scholar
  118. Meng, X., Liu, Y., Gao, X., & Zhang, H. (2014). A new bio-inspired algorithm: Chicken swarm optimization. In Y. Tan, Y. Shi, & C. C. Coello (Eds.), Advances in swarm intelligence (Vol. 8794, pp. 86–94)., Lecture notes in computer science New York, USA: Springer.Google Scholar
  119. Merrikh-Bayat, F. (2015). The runner-root algorithm: A metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature. Applied Soft Computing, 33, 292–303.CrossRefGoogle Scholar
  120. Min, H., & Wang, Z. (2010). Group escape behavior of multiple mobile robot system by mimicking fish schools. Paper presented at the IEEE International Conference on Robotics and Biomimetics (ROBIO), December 14–18, Tianjin, China, pp. 320–326.Google Scholar
  121. Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 83, 80–98.CrossRefGoogle Scholar
  122. Mirjalili, S. (in press-a). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput & Application. doi: 10.1007/s00521-015-1920-1.
  123. Mirjalili, S. (in press-b). Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowledge-Based Systems.
  124. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.Google Scholar
  125. Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (in press). Multi-verse optimizer: a nature inspired algorithm for global optimization. Neural Computing & Application.
  126. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.CrossRefGoogle Scholar
  127. Moosavian, N., & Roodsari, B. K. (2014). Soccer league competition algorithm: A novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm and Evolutionary Computation, 17, 14–24.CrossRefGoogle Scholar
  128. Mora-Gutiérrez, R. A., Ramírez-Rodríguez, J., & Rincón-García, E. A. (in press). An optimization algorithm inspired by musical composition. Artificial Intelligence Review. doi: 10.1007/s10462-011-9309-8.
  129. Mucherino, A., & Seref, O. (2007). Monkey search: A novel metaheuristic search for global optimization. AIP Conference Proceedings, 953(1), 162–173.CrossRefGoogle Scholar
  130. Müller, S. D., Marchetto, J., Airaghi, S., & Koumoutsakos, P. (2002). Optimization based on bacterial chemotaxis. IEEE Transactions on Evolutionary Computation, 6(1), 16–29.CrossRefGoogle Scholar
  131. Muñoz, M. A., López, J. A., & Caicedo, E. (2009). An artificial beehive algorithm for continuous optimization. International Journal of Intelligent Systems, 24, 1080–1093.zbMATHCrossRefGoogle Scholar
  132. Murase, H. (2000). Finite element inverse analysis using a photosynthetic algorithm. Computers and Electronics in Agriculture, 29, 115–123.CrossRefGoogle Scholar
  133. Mutazono, A., Sugano, M., & Murata, M. (2012). Energy efficient self-organizing control for wireless sensor networks inspired by calling behavior of frogs. Computer Communications, 35, 661–669.CrossRefGoogle Scholar
  134. Nara, K., Takeyama, T., & Kim, H. (1999). A new evolutionary algorithm based on sheep flocks heredity model and its application to scheduling problem. Paper presented at the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. VI-503–VI-508.Google Scholar
  135. Neshat, M., Sepidnam, G., & Sargolzaei, M. (in press). Swallow swarm optimization algorithm: A new method to optimization. Neural Computing & Application. doi: 10.1007/s00521-012-0939-9.
  136. Nishida, T. Y. (2005). Membrane algorithm: An approximate algorithm for NP-complete optimization problems exploiting P-systems. Paper presented at the 6th International workshop on membrane computing (WMC), July 18–21, Vienna, Austria, pp. 26–43.Google Scholar
  137. Niu, B., & Wang, H. (2012). Bacterial colony optimization. Discrete Dynamics in Nature and Society, 2012, 1–28.zbMATHGoogle Scholar
  138. Oftadeh, R., Mahjoob, M. J., & Shariatpanahi, M. (2010). A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search. Computers & Mathematics with Applications, 60, 2087–2098.zbMATHCrossRefGoogle Scholar
  139. Pan, W.-T. (2012). A new fruit fly optimization algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74.CrossRefGoogle Scholar
  140. Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control System Management, 22(3), 52–67.CrossRefGoogle Scholar
  141. Patel, V. K., & Savsani, V. J. (2015). Heat transfer search (HTS): A novel optimization algorithm. Information Sciences, 324, 217–246.CrossRefGoogle Scholar
  142. Pattnaik, S. S., Bakwad, K. M., Sohi, B. S., Ratho, R. K., & Devi, S. (2013). Swine influenza models based optimization (SIMBO). Applied Soft Computing, 13(1), 628–653.CrossRefGoogle Scholar
  143. Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm—A novel tool for complex optimisation problems. Paper presented at the Second International Virtual Conference on Intelligent production machines and systems (IPROMS), pp. 454–459.Google Scholar
  144. Premaratne, U., Samarabandu, J., & Sidhu, T. (2009). A new biologically inspired optimization algorithm. Paper presented at the Fourth International Conference on Industrial and Information Systems (ICIIS), December 28–31, Sri Lanka, pp. 279–284.Google Scholar
  145. Qi, X., Zhu, Y., & Zhang, H. (in press). A new meta-heuristic butterfly-inspired algorithm. Journal of Computational Science.
  146. Quijano, N., & Passino, K. M. (2010). Honey bee social foraging algorithms for resource allocation: Theory and application. Engineering Applications of Artificial Intelligence, 23, 845–861.CrossRefGoogle Scholar
  147. Rabanal, P., Rodríguez, I., & Rubio, F. (2007). Using river formation dynamics to design heuristic algorithms. In C. S. C. S. G. Akl, M. J. Dinneen, G. Rozenber, & H. T. Wareham (Eds.), UC 2007, LNCS (Vol. 4618, pp. 163–177). Heidelberg: Springer.Google Scholar
  148. Rajabioun, R. (2011). Cuckoo optimization algorithm. Applied Soft Computing, 11, 5508–5518.CrossRefGoogle Scholar
  149. Rao, R. V. (2016). Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7, 1–16.Google Scholar
  150. Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43, 303–315.CrossRefGoogle Scholar
  151. Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179, 2232–2248.zbMATHCrossRefGoogle Scholar
  152. Ray, K. S. (2012). Soft computing approach to pattern classification and object recognition: A unified concepts. New York, Heidelberg, Dordrecht, London: Springer Science + Business Media. ISBN 978-1-4614-5347-5.zbMATHCrossRefGoogle Scholar
  153. 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(4), 386–396.CrossRefGoogle Scholar
  154. Reynolds, R. G. (1994). An introduction to cultural algorithms. Paper presented at the 3rd Annual Conference on Evolutionary Programming, pp. 131–139.Google Scholar
  155. Sacco, W. F., & de Oliveira, C. R. E. (2005). A new stochastic optimization algorithm based on a particle collision metaheuristic. Paper presented at the 6th World Congresses of Structural and Multidisciplinary Optimization, Rio de Janeiro, Brazil, 30 May–03 June, pp. 1–6.Google Scholar
  156. Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2012). Mine blast algorithm for optimization of truss structures with discrete variables. Computers & Structures, 102–103, 49–63.CrossRefGoogle Scholar
  157. Salem, S. A. (2012). BOA: A novel optimization algorithm. Paper presented at the International Conference on Engineering and Technology (ICET), October 10–11, Cairo, Egypt, pp. 1–5.Google Scholar
  158. Salimi, H. (2015). Stochastic fractal search: A powerful metaheuristic algorithm. Knowledge-Based Systems, 75, 1–8.CrossRefGoogle Scholar
  159. Saremi, S., Mirjalili, S., & Lewis, A. (2017). Grasshopper optimisation algorithm: Theory and application. Advances in Engineering Software, 105, 30–47.CrossRefGoogle Scholar
  160. Sato, T., & Hagiwara, M. (1997). Bee system: Finding solution by a concentrated search. Paper presented at the IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3954–3959.Google Scholar
  161. Savsani, P., & Savsani, V. (in press). Passing vehicle search (PVS): A novel metaheuristic algorithm. Applied Mathematical Modelling. doi: 10.1016/j.apm.2015.10.040.
  162. Shah-Hosseini, H. (2007). Problem solving by intelligent water drops. Paper presented at the IEEE Congress on Evolutionary Computation (CEC), September 25–28, pp. 3226–3231.Google Scholar
  163. Shah-Hosseini, H. (2011). Otsu’s criterion-based multilevel thresholding by a nature-inspired metaheuristic called galaxy-based search algorithm. Paper presented at the Third World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 383–388.Google Scholar
  164. Shann, M. (2008). Emergent behavior in a simulated robot inspired by the slime mold (Unpublished Bachelor Thesis), University of Zurich, Zurich, ZH, Switzerland.Google Scholar
  165. Sharafi, Y., Khanesar, M. A., & Teshnehlab, M. (2016). COOA: Competitive optimization algorithm. Swarm and Evolutionary Computation, 30, 39–63.CrossRefGoogle Scholar
  166. Shareef, H., Ibrahim, A. A., & Mutlag, A. H. (2015). Lightning search algorithm. Applied Soft Computing, 36, 315–333.CrossRefGoogle Scholar
  167. Shen, J., & Li, Y. (2009). Light ray optimization and its parameter analysis. Paper presented at the International Joint Conference on Computational Sciences and Optimization (CSO), April 24–26, Sanya, China, pp. 918–922.Google Scholar
  168. Shi, Y. (2011). Brain storm optimization algorithm. In Y. Tan, Y. Shi, & G. Wang (Eds.), ICSI 2011, Pat I, LNCS 6728 (pp. 303–309). Berlin, Heidelberg: Springer.Google Scholar
  169. Simon, D. (2008). Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702–713.CrossRefGoogle Scholar
  170. Steinbuch, R. (2011). Bionic optimisation of the earthquake resistance of high buildings by tuned mass dampers. Journal of Bionic Engineering, 8, 335–344.CrossRefGoogle Scholar
  171. Su, M.-C., Su, S.-Y., & Zhao, Y.-X. (2009). A swarm-inspired projection algorithm. Pattern Recognition, 42, 2764–2786.zbMATHCrossRefGoogle Scholar
  172. Sulaiman, M., & Salhi, A. (2015). A seed-based plant propagation algorithm: The feeding station model. The Scientific World Journal, 1–16 (Article ID 904364).Google Scholar
  173. Sun, J., & Lei, X. (2009). Geese-inspired hybrid particle swarm optimization algorithm. Paper presented at the International Conference on Artificial Intelligence and Computational Intelligence, pp. 134–138.Google Scholar
  174. Tabari, A., & Ahmad, A. (2017). A new optimization method: Electro-search algorithm. Computers & Chemical Engineering, 103, 1–11.CrossRefGoogle Scholar
  175. Tadeusiewicz, R. (2011). Introduction to intelligent systems. In B. M. Wilamowski & J. D. Irwin (Eds.), Intelligent systems (pp. 1–12). USA: CRC Press, Taylor and Francis Group, LLC. ISBN 978-1-4398-0283-0 (Chapter 1).Google Scholar
  176. Taherdangkoo, M., Shirzadi, M. H., & Bagheri, M. H. (2012). A novel meta-heuristic algorithm for numerical function optimization_blind, naked mole-rats (BNMR) algorithm. Scientific Research and Essays, 7(41), 3566–3583.CrossRefGoogle Scholar
  177. Taherdangkoo, M., Yazdi, M., & Bagheri, M. H. (2011). Stem cells optimization algorithm. LNBI 6840 (pp. 394–403). Berlin, Heidelberg: Springer.Google Scholar
  178. Tan, Y., & Zhu, Y. (2010). Fireworks algorithm for optimization. In Y. Tan, Y. Shi, & K. C. Tan (Eds.), ICSI 2010, Part I, LNCS 6145 (pp. 355–364). Berlin, Heidelberg: Springer.Google Scholar
  179. Tang, D., Dong, S., Jiang, Y., Li, H., & Huang, Y. (2015). ITGO: Invasive tumor growth optimization algorithm. Applied Soft Computing, 36, 670–698.CrossRefGoogle Scholar
  180. Tay, D., Poh, C. L., & Kitney, R. I. (2015). A novel neural-inspired learning algorithm with application to clinical risk prediction. Journal of Biomedical Informatics, 54, 305–314.CrossRefGoogle Scholar
  181. Tayarani, M. H., & Akbarzadeh, M. R. (2008). Magnetic optimization algorithms a new synthesis. Paper presented at the IEEE Congress on Evolutionary Computation (CEC), pp. 2659–2664.Google Scholar
  182. Teodorović, D., & Dell’Orco, M. (2005). Bee colony optimization: A cooperative learning approach to complex transportation problems. Paper presented at the 16th Mini-EURO Conference on Advanced OR and AI Methods in Transportation, pp. 51–60.Google Scholar
  183. Thammano, A., & Moolwong, J. (2010). A new computational intelligence technique based on human group formation. Expert Systems with Applications, 37, 1628–1634.CrossRefGoogle Scholar
  184. The Economist. (2016). Artificial intelligence: March of the machines. The Economist, 419(8995), 12.Google Scholar
  185. Theraulaz, G., Goss, S., Gervet, J., & Deneubourg, J. L. (1991). Task differentiation in polistes wasps colonies: a model for self-organizing groups of robots. Paper presented at the First International Conference on Simulation of Adaptive Behavior, pp. 346–355.Google Scholar
  186. Topal, A. O., & Altun, O. (2016). A novel meta-heuristic algorithm: Dynamic virtual bats algorithm. Information Sciences, 354, 222–235.CrossRefGoogle Scholar
  187. Varaee, H., & Ghasemi, M. R. (in press). Engineering optimization based on ideal gas molecular movement algorithm. Engineering Computations. doi: 10.1007/s00366-016-0457-y.
  188. Wang, G.-G. (in press). Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing. doi: 10.1007/s12293-016-0212-3.
  189. Wang, G.-G., Deb, S., & Coelho, L. d. S. (in press). Earthworm optimisation algorithm: A bio-inspired metaheuristic algorithm for global optimisation problems. International Journal of Bio-Inspired Computation.
  190. Wang, G.-G., Deb, S., & Cui, Z. (in press). Monarch butterfly optimization. Neural Computing & Application. doi: 10.1007/s00521-015-1923-y.
  191. Wang, G. G., Deb, S., Gao, X. Z., & Coelho, L. D. S. (in press). A new metaheuristic optimization algorithm motivated by elephant herding behavior. International Journal of Bio-Inspired Computation.Google Scholar
  192. Wedde, H. F., Farooq, M., & Zhang, Y. (2004). Beehive: An efficient fault-tolerant routing algorithm inspired by honey bee behavior. In M. Dorigo (Ed.), ANTS 2004, LNCS 3172 (pp. 83–94). Berlin, Heidelberg: Springer.Google Scholar
  193. Wei, Z. H., Cui, Z. H., & Zeng, J. C. (2010). Social cognitive optimization algorithm with reactive power optimization of power system. 2010 International Conference on Computational Aspects of Social Networks (CASoN), September 26–28, Taiyuan, China, pp. 11–14.Google Scholar
  194. Xie, L.-P., & Zeng, J.-C. (2009). A global optimization based on physicomimetics framework. Paper presented at the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation (GEC), June 12–14, Shanghai, China, pp. 609–616.Google Scholar
  195. Xing, B. (2014a). Novel computational intelligence for optimizing cyber physical pre-evaluation system. In Z. H. Khan, A. B. M. S. Ali & Z. Riaz (Eds.), Computational intelligence for decision support in cyber-physical systems (Chapter 15, pp. 449–464). Singapore, Heidelberg, New York, Dordrecht, London: Springer Science + Business Media. ISBN 978-981-4585-35-4.Google Scholar
  196. Xing, B. (2014b). The optimization of computational stock market model based complex adaptive cyber physical logistics system. In Z. H. Khan, A. B. M. S. Ali & Z. Riaz (Eds.), Computational intelligence for decision support in cyber-physical systems (Chapter 12, pp. 357–380). Singapore, Heidelberg, New York, Dordrecht, London: Springer Science + Business Media. ISBN 978-981-4585-35-4.Google Scholar
  197. Xing, B. (2015a). Novel nature-derived intelligent algorithms and their applications in antenna optimization. In M. A. Matin (Ed.), Wideband, multiband, and smart reconfigurable antennas for modern wireless communications (Chapter 10, pp. 296–339). Hershey: IGI Global. ISBN 978-1-4666-8645-8.Google Scholar
  198. Xing, B. (2015b). Optimization in production management: economic load dispatch of cyber physical power system using artificial bee colony. In C. Kahraman & S. Ç. Onar (Eds.), Intelligent techniques in engineering management: Theory and applications (Chapter 12, pp. 275–293). Cham, Heidelberg, New York, Dordrecht, London, Switzerland: Springer International Publishing. ISBN 978-3-319-17905-6.Google Scholar
  199. Xing, B. (2016a). Agent-based machine-to-machine connectivity analysis for the Internet of things environment. In Z. Mahmood (Ed.), Connectivity frameworks for smart devices: The internet of things from a distributed computing perspective (pp. 43–61). Switzerland: Springer International Publishing. Chapter 3. ISBN 978-3-319-33122-5.CrossRefGoogle Scholar
  200. Xing, B. (2016b). Smart robot control via novel computational intelligence methods for ambient assisted living. In K. K. Ravulakollu, M. A. Khan, & A. Abraham (Eds.), Trends in ambient intelligent systems (pp. 29–55). Switzerland: Springer International Publishing. Chapter 2. ISBN 978-3-319-30184-6.CrossRefGoogle Scholar
  201. Xing, B. (2016c). The spread of innovatory nature originated metaheuristics in robot swarm control for smart living environments. In H. E. P. Espinosa (Ed.), Nature-Inspired computing for control systems (pp. 39–70). Cham, Heidelberg, New York, Dordrecht, London, Switzerland: Springer International Publishing. ISBN 978-3-319-26228-4 (Chapter 3).CrossRefGoogle Scholar
  202. Xing, B. (2017a). Protecting mobile payments security: A case study. In W. Meng, X. Luo, S. Furnell & J. Zhou (Eds.), Protecting mobile networks and devices: Challenges and solutions. USA: CRC Press, Taylor & Francis Group, LLC. ISBN 978-1-4987-3583-4.Google Scholar
  203. Xing, B. (2017b). Visible light based throughput downlink connectivity for the cognitive radio networks. In M. A. Matin (Ed.), Spectrum access and management for cognitive radio networks (pp. 211–232). Springer Science + Business Media: Singapore. Chapter 8. ISBN 978-981-10-2253-1.CrossRefGoogle Scholar
  204. Xing, B., & Gao, W.-J. (2014a). Computational intelligence in remanufacturing. Hershey: IGI Global. ISBN 978-1-4666-4908-8.Google Scholar
  205. Xing, B., & Gao, W.-J. (2014b). Innovative computational intelligence: A rough guide to 134 clever algorithms. Cham, Heidelberg, New York, Dordrecht, London, Switzerland: Springer International Publishing. ISBN 978-3-319-03403-4.zbMATHCrossRefGoogle Scholar
  206. Yan, G.-W., & Hao, Z. (2012). A novel atmosphere clouds model optimization algorithm. Paper presented at the International Conference on Computing, Measurement, Control and Sensor Network (CMCSN), July 7–9, Taiyuan, China, pp. 217–220.Google Scholar
  207. Yan, J., Zhang, J., Liu, Y., Han, S., Li, L., & Gu, C. (2015). Unit commitment in wind farms based on a glowworm metaphoralgorithm. Electric Power Systems Research, 129, 94–104.CrossRefGoogle Scholar
  208. Yang, C., Tu, X., & Chen, J. (2007). Algorithm of marriage in honey bees optimization based on the wolf pack search. Paper presented at the International Conference on Intelligent Pervasive Computing (IPC), pp. 462–467.Google Scholar
  209. Yang, F.-C., & Wang, Y.-P. (2007). Water flow-like algorithm for object grouping problems. Journal of the Chinese Institute of Industrial Engineers, 24(6), 475–488.CrossRefGoogle Scholar
  210. Yang, J., & Waller, M. P. (2017). A hybrid dimer swarm optimizer. Computational and Theoretical Chemistry, 1102, 98–104.CrossRefGoogle Scholar
  211. Yang, X. S. (2005). Engineering optimizations via nature-inspired virtual bee algorithms IWINAC 2005, lNCS 3562 (Vol. 3562/2005, pp. 317–323). Berlin: Springer.Google Scholar
  212. Yang, X.-S. (2010). A new metaheuristic bat-inspired clgorithm. Paper presented at the Nature Inspired Cooperative Strategies for Optimization (NISCO 2010), Studies in Computational Intelligence, SCI 284, pp. 65–74.Google Scholar
  213. Yang, X.-S. (2012). Flower pollination algorithm for global optimization. Unconventional computation and natural computation, LNCS 7445 (pp. 240–249). Berlin: Springer.Google Scholar
  214. Yang, X.-S., & Deb, S. (2009). Cuckoo search via Lévy flights. Paper presented at the World Congress on Nature & Biologically Inspired Computing (NaBIC), India, December 9–11, 2009, pp. 210–214.Google Scholar
  215. 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 284 (pp. 101–111). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  216. Yazdani, M., & Jolai, F. (2016). Lion optimization algorithm(LOA): A nature-inspired metaheuristic algorithm. Journal of Computational Design and Engineering, 3, 24–36.CrossRefGoogle Scholar
  217. Yeh, W. C. (2012). Novel swarm optimization for mining classification rules on thyroid gland data. Information Sciences, 197, 65–76.CrossRefGoogle Scholar
  218. Zaránd, G., Pázmándi, F., Pál, K. F., & Zimányi, G. T. (2002). Using hysteresis for optimization. Physical Review Letters, 89(15), 150201–150204.Google Scholar
  219. Zhang, W., Luo, Q., & Zhou, Y. (2009). A method for training RBF neural networks based on population migration algorithm. Paper presented at the International Conference on Artificial Intelligence and Computational Intelligence (AICI), pp. 165–169.Google Scholar
  220. Zhang, X., Chen, W., & Dai, C. (2008). Application of oriented search algorithm in reactive power optimization of power system. Paper presented at the DRPT2008, 6-9 April Nanjing, China, PP. 2856–2861.Google Scholar
  221. Zhang, X., Huang, S., Hu, Y., Zhang, Y., Mahadevan, S., & Deng, Y. (2013). Solving 0-1 knapsack problems based on amoeboid organism algorithm. Applied Mathematics and Computation, 219, 9959–9970.MathSciNetzbMATHCrossRefGoogle Scholar
  222. Zhang, X., Sun, B., Mei, T., & Wang, R. (2010). Post-disaster restoration based on fuzzy preference relation and bean optimization algorithm. Paper presented at the IEEE Youth Conference on Information Computing and Telecommunications (YC-ICT), November 28–30, pp. 271–274.Google Scholar
  223. Zheng, M., Liu, G.-X., Zhou, C.-G., Liang, Y.-C., & Wang, Y. (2010). Gravitation field algorithm and its application in gene cluster. Algorithms for Molecular Biology, 5(32), 1–11.CrossRefGoogle Scholar
  224. 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(4), 869–876.Google Scholar
  225. Zhu, C., & Ni, J. (2012). Cloud model-based differential evolution algorithm for optimization problems. Paper presented at the Sixth International Conference on Internet Computing for Science and Engineering (ICICSE), April 21–23, Henan, China, pp. 55–59.Google Scholar
  226. Zhu, G.-Y., & Zhang, W.-B. (2017). Optimal foraging Algorithm for global optimization. Applied Soft Computing, 51, 294–313.CrossRefGoogle Scholar
  227. 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.CrossRefGoogle Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Engineering and the Built EnvironmentInstitute for Intelligent System, University of JohannesburgJohannesburgSouth Africa
  2. 2.Faculty of Engineering and the Built Environment, Department of Electrical and Electronic Engineering SciencesUniversity of JohannesburgJohannesburgSouth Africa

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