Abstract
Exploration and exploitation are two important factors to consider in the design of optimization techniques. Two new techniques are introduced for particle swarm optimization: “resets” increase exploitation and “delayed updates” increase exploration. In general, the added exploitation with resets helps more with the lbest topology which is more explorative, and the added exploration with delayed updates helps more with the gbest topology which is more exploitive.
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
Preview
Unable to display preview. Download preview PDF.
References
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Cartwright, L., Hendtlass, T.: A Heterogeneous Particle Swarm. In: Korb, K., Randall, M., Hendtlass, T. (eds.) Proceedings of Fourth Australian Conference on Artificial Life, pp. 201–210. Springer, Heidelberg (2009)
Chen, S.: Locust Swarms – A New Multi-Optima Search Technique. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation, pp. 1745–1752 (2009)
Hendtlass, T.: WoSP: A Multi-Optima Particle Swarm Algorithm. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, pp. 727–734 (2005)
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Dordrecht (1997)
Glover, F.: Tabu Search. ORSA Journal on Computing 1, 190–206 (1989)
Glover, F.: Tabu Search Part II. ORSA Journal on Computing 2(1), 4–32 (1990)
Hansen, P., Mladenović, N.: An Introduction to variable neighborhood search. In: Voß, S., Martello, S., Osman, I., Roucairol, C. (eds.) Methaheuristics: Advances and trends in local search paradigms for optimization, ch.30, pp. 433–458. Kluwer Academic Publishers, Dordrecht (1999)
Stützle, T.: Iterated local search for the quadratic assignment problem. Technical report, aida-99-03, FG Intellektik, TU Darmstadt (1999)
Lourenço, H.R., Martin, O., Stützle, T.: A beginnerś introduction to Iterated Local Search. In: Proceedings of MIC 2001 Metaheuristics International Conference, Porto, Portugal, vol. 1, pp. 1–6 (2001)
Dorigo, M., Gambardella, L.: Ant Colony System: a cooperative learning approach to the traveling salesman problem. IEEE Transaction on Evolutionary Computation 1, 53–66 (1997)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)
Rechenberg, I.: Evolutionsstrategie – Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis). Fromman-Holzboog (1973)
Eberhart, R.C., Kennedy, J.: A New Optimizer using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43. IEEE Service Center, Piscataway (1995)
Bratton, D., Kennedy, J.: Defining a Standard for Particle Swarm Optimization. In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS 2007), pp. 120–127 (2007)
Kalyanmoy, D.: Multi-Objective Optimization using Evolutionary Algorithms. Department of Mechanical Engineering. Institute of Technology, Kanpur, India (2001)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
Aarts, E.H.L., Korst, J.H.M., Laarhoven, P.J.M.: Simulated Annealing. In: Local Search in Combinatorial Optimization. In: Aarts, E.H.L., Lenstra, J.K. (eds.) Local Search in Combinatorial Optimization, pp. 91–120. Wiley Interscience, Chichester (1997)
Blum, C., Roli, A.: Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison
Moscato, P., Cotta, C.: An Introduction to Memetic Algorithms. Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial 19, 131–148 (2003)
Hansen, N., Finck, S., Ros, R., Auger, A.: Real-Parameter Black-Box Optimization Benchmarking 2009: Noiseless Functions Definitions. INRIA Technical Report RR-6829 (2009)
El-Abd, M., Kamel, M.S.: Black-Box Optimization Benchmarking for Noiseless Function Testbed using Particle Swarm Optimization. In: Proceedings of the 2009 Genetic and Evolutionary Computation Conference, pp. 2269–2273 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Noa Vargas, Y., Chen, S. (2010). Particle Swarm Optimization with Resets – Improving the Balance between Exploration and Exploitation. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-16773-7_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16772-0
Online ISBN: 978-3-642-16773-7
eBook Packages: Computer ScienceComputer Science (R0)