Abstract
A hybrid approach called Evolutionary Swarm Cooperative Algorithm (ESCA) based on the collaboration between a particle swarm optimization algorithm and an evolutionary algorithm is presented. ESCA is designed to deal with moving optima of optimization problems in dynamic environments. ESCA uses three populations of individuals: two EA populations and one Particle Swarm Population. The EA populations evolve by the rules of an evolutionary multimodal optimization algorithm being used to maintain the diversity of the search. The particle swarm confers precision to the search process. The efficiency of ESCA is evaluated by means of numerical experiments.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Banks A, Vincent J, Anyakoha C (2007) A review of particle swarm optimization. Part I: background and development. Nat Comput 6(4):467–484. doi: 10.1007/s11047-007-9049-5
Blackwell T, Branke J (2006) Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evol Comput 10(4):459–472
Branke J (2001) Evolutionary optimization in dynamic environments. Klüwer, Norwell
Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments—a survey. IEEE Trans Evol Comput 9(3):303–317
Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
Li X, Branke J, Blackwell T (2006) Particle swarm with speciation and adaptation in a dynamic environment. In: GECCO ’06: proceedings of the 8th annual conference on genetic and evolutionary computation, pp 51–58. ACM Press, New York, NY, USA. doi:10.1145/1143997.1144005
Lung RI, Dumitrescu D (2007) A new collaborative evolutionary-swarm optimization technique. In: GECCO’07: proceedings of the 2007 GECCO conference companion on genetic and evolutionary computation, pp 2817–2820. ACM Press, New York, NY, USA. doi:10.1145/1274000.1274043
Moser I (2007) All currently known publications on approaches which solve the moving peaks problem. http://www.aifb.uni-karlsruhe.de/~jbr/MovPeaks/movpeaks_revi ew.pdf
Moser I, Hendtlass T (2007) A simple and efficient multi-component algorithm for solving dynamic function optimisation problems. CEC 2007, IEEE congress on evolutionary computation, pp 252–259. doi:10.1109/CEC.2007.4424479
Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Tech. Rep. TR-95-012, Berkeley, CA. http://www.citeseer.ist.psu.edu/article/storn95differential.html
Storn R, Price K (1997) Differential evolution a simple evolution strategy for fast optimization. Dr. Dobb’s J Softw Tools 22(4):18–24
Thomsen R (2004) Multimodal optimization using crowding-based differential evolution. In: Proceedings of the 2004 IEEE congress on evolutionary computation, pp 1382–1389. IEEE Press, Portland, OR, USA
Tsutsui S, Fujimoto Y, Gosh A (1997) Forking genetic algorithms: GAs with search space division. Evol Comput 5:61–80
Ursem RK (2000) Multinational GAs: multimodal optimization techniques in dynamic environments. In: Proceedings of the second genetic and evolutionary computation conference (GECCO-2000), vol 1, pp 19–26. Morgan Kauffmann Publishers, Riviera Hotel, Las Vegas, USA
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lung, R.I., Dumitrescu, D. Evolutionary swarm cooperative optimization in dynamic environments. Nat Comput 9, 83–94 (2010). https://doi.org/10.1007/s11047-009-9129-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11047-009-9129-9