Abstract
In this chapter, I present those methods that generate a set of solutions from one iteration (known as generation) to the next instead of one only solution at a time. I give an overview of the most commonly used approaches in this particular class. These include genetic algorithms, harmony search, scatter search, differential evolution, ant systems, bees algorithm and particle swarm. The first four can be categorised under evolutionary computing, whereas the other three under swarm intelligence. For completeness, I also briefly mention other ones such as path relinking, heuristic cross-entropy, artificial immune systems, plant propagation and the psycho-clonal algorithm. Note that some of these heuristics can also be considered under the previous class, but to emphasise the power of having multiple solutions simultaneously, these are conveniently categorised under this class instead.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alp, O., Erkut, E., & Drezner, Z. (2003). An efficient genetic algorithm for the p-median problem. Annals of Operations Research, 122, 21–42.
Bakhouya, M., & Gaber, J. (2007). An Immune inspired-based optimisation algorithm: Application to the travelling salesman problem. Advanced Modeling and Optimization, 9, 105–116.
Bullnheimer, B., Harlt, R., & Strauss, C. (1998). Applying ant systems to the vehicle routing problem. In S. Voss, S. Martello, I. H. Osman, & C. Roucairol (Eds.), Metaheuristics: Advances and trends in local search paradigms for optimization (pp. 285–296). Boston: Kluwer.
Caserta, M., & Quinonez Rico, E. (2009). A cross entropy-based metaheuristic algorithm for large scale capacitated facility location problems. The Journal of the Operational Research Society, 60, 1439–1448.
de Castro, L. N., & Timmis, J. (2002). Artificial immune systems: A New Computational Intelligent Approach. London:Springer.
Colorni, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by Ant Colonies. In F. Varela & P. Bourgine (Eds.), Proceedings of the European conference on artificial life (pp. 457–474). Amsterdam: Elsevier Publishing.
Daneubourg, J. L., Aron, A., Goss, S., & Pasteels, J. M. (1990). The self organising exploratory pattern of the argentine ant. Journal of Insect Behavior, 3, 159–168.
Das, S., Abraham, A., Chakraborty, U. K., & Konar, A. (2009). Differential evolution with a neighbourhood-based mutation operator. IEEE Transactions on Evolutionary Computation, 13, 526–553.
Dasgupta, D. (Ed.). (1999). Artificial immune system and their applications. Berlin/New York: Springer.
De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Doctoral dissertation, University of Michigan, Ann Arbor.
Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation, 1, 53–66.
Dorigi, M., & Stutzle, T. (2010). Ant colony optimization: Overview and recent advances. In M. Gendreau & J. Y. Potvin (Eds.), Handbook of metaheuristics (2nd ed., pp. 227–264). London: Springer.
Dorigo, M., Caro, G., & Gambardella, L. (1999). Ant algorithms for discrete optimization. Art Life, 5, 137–172.
Fletcher, R. (1987). Practical methods of optimisation (2nd ed.). Chichester: Wiley, 2001 reprint.
Garcia-Villoria, A., & Pastor, R. (2009). Introducing dynamic diversity into a discrete particle swarm optimization. Computers and Operations Research, 36, 951–966.
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Simulation, 76(2), 60–68.
Glover, F., & Laguna, M. (1997). Tabu search. Boston: Kluwer.
Glover, F., Laguna, M., & Marti, R. (2003). Scatter search and path relinking: Advances and applications. In F. Glover & G. A. Kochenberger (Eds.), Handbook of metaheuristics (pp. 1–35). London: Kluwer.
Goldberg, D. E. (1989). Genetic algorithm in search, optimization and machine learning. New York: Addison-Wesley.
Goldberg, D. E., & Lingle, R. (1975). Alleles, loci and the travelling salesman problem. In J. J. Grefenstette (Ed.), Proceedings of an international conference on genetic algorithms and their applications (pp. 154–159). Hillsdale: Lawrence Erlbaum Associates.
Gomez, A., Amran, I., & Salhi, S. (2013). Solution of classical transport problems with bee algorithms. International Journal of Logistics Systems and Management, 15, 160–170.
Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Harbor: University of Michigan Press.
Ishida, Y., Hirayama, H., Fujita, H., Ishiguro, A., & Mori, K. (Eds.) (1998). Immunity-based systems-intelligent systems by artificial immune systems. Tokyo: Corona Pub. Co.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Computer Engineering Department, Erciyes University.
Kennedy, J., & Eberhault, R. C. (1995). Particle swarm optimization. IEEE international conference on neural networks, Perth, pp. 1942–1948.
Lu, H., & Chen, W. (2006). Dynamic-objective particle swarm optimization for constrained optimization problems. Journal of Combinatorial Optimization, 12, 408–419.
Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188, 1567–1579.
Marti, R., Laguna, M., & Glover, F. (2006). Principles of scatter search. European Journal of Operational Research, 169, 359–372.
Maslow, A. H. (1954). Motivation and personality. New York: Harper & Sons.
Neri, F., & Tirronen, V. (2010). Recent advances in differential evolution: A survey and experimental analysis. Artificial Intelligence Review, 33, 61–106.
Pan, Q. K., Suganthan, P. N., Tasgetiren, M. F., & Liang, J. J. (2010). A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation, 216, 830–848.
Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2006). The bees algorithm, a novel rool for complex optimisation problems. In Proceedings of the 2nd virtual international conference on intelligent production machines and systems, Elsevier, pp. 454–459.
Resende, M. G. C., & Ribiero, C. G. (2005). Scatter search and path-relinking: Fundamentals, advances, and applications. In M. Gendreau & J. Y. Potvin (Eds.), Handbook of metaheuristics (2nd ed., pp. 87–107). London: Springer.
Rubinstein, R. Y. (1997). Optimization of computer simulation models with rare events. European Journal of Operational Research, 99, 89–112.
Rubinstein, R. Y., & Kroese, D. P. (2004). The cross-entropy method: A unified approach to combinatorial optimization, Monte Carlo simulation and machine learning. New York: Springer.
Salhi, A., & Fraga, E. S. (2011). Nature-inspired optimisation approaches and the new plant propagation algorithm. In Proceedings of the ICeMATH2011, pp. K2–1 to K2–8.
Salhi, S., & Gamal, M. D. H. (2003). A genetic algorithm based approach for the uncapacitated continuous location-allocation problem. Annals of Operations Research, 123, 203–222.
Salhi, S., & Petch, R. (2007). A GA based heuristic for the vehicle routing problem with multiple trips. Journal of Mathematical Modelling and Algorithms, 6, 591–613.
Selamoglu, B. I., & Salhi, A. (2016). The plant propagation algorithm for discrete optimisation: The case of the travelling salesman problem. In X. S. Yang (Ed.), Nature-inspired computation in engineering (Studies in computational intelligence, Vol. 637, pp. 43–61). Switzerland: Springer.
Storn, R., & Price, K. (1997). Differential evolution- a simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.
Stützle, T., & Hoos, H. H. (2000). MAX-MIN ant system. Future Generation Computer Systems, 16, 889–914.
Sulaiman, M., Salhi, A., & Selamoglu, B. I. (2014). A plant propagation algorithm for constrained engineering optimisation problems. Mathematical Problems in Engineering, 2014, 1–17.
Sulaiman, M., & Salhi, A. (2016). A hybridisation of runner-based and seed-based plant propagation algorithms. In X. S. Yang (Ed.), Nature-inspired computation in engineering (Studies in computational intelligence, Vol. 637, pp. 1–18). Switzerland: Springer.
Szeto, W. Y., Wu, Y., & Ho, S. C. (2011). An artificial bee colony algorithm for the capacitated vehicle routing problem. European Journal of Operational Research, 215, 126–135.
Tarantilis, C. D., & Kiranoudis, C. T. (2002). BoneRoute: An adaptive memory-based method for effective fleet management. Annals of Operations Research, 115, 227–241.
Tiwari, M. K., Prakash, A., Kumar, A., & Mileham, A. R. (2005). Determination of an optimal sequence using the psychoclonal algorithm. Journal of Engineering Manufacture, 219, 137–149.
Wade, A. C., & Salhi, S. (2003). An ant system algorithm for the mixed vehicle routing problem with backhauls. In M. G. Resende & J. P. de Sousa (Eds.), Metaheuristics: Computer decision-making (pp. 699–719). New York: Kluwer.
Wedde, H. F., Farooq, M., & Zhang, Y. (2004). BeeHive: An efficient fault-tolerant routing algorithm inspired by honey bee behavior. In Ant colony optimization and swarm intelligence (Lecture notes in computer science, Vol. 3172, pp. 83–94). Berlin: Springer.
Yang, X.-S. (2005). Engineering optimizations via nature-inspired virtual bee algorithms. In J. Mira & J. R. Alvarez (Eds.), IWINAC 2005 (Lecture notes in computer science, Vol. 3562, pp. 317–323). Berlin/Heidelberg: Springer.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2017 The Author(s)
About this chapter
Cite this chapter
Salhi, S. (2017). Population-Based Heuristics. In: Heuristic Search. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-49355-8_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-49355-8_4
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-319-49354-1
Online ISBN: 978-3-319-49355-8
eBook Packages: Business and ManagementBusiness and Management (R0)