Abstract
In this paper a novel hybridization of agent-based evolutionary system (EMAS, a metaheuristic putting together agency and evolutionary paradigms) is presented. This method assumes utilization of particle swarm optimization (PSO) for upgrading certain agents used in the EMAS population, based on agent-related condition. This may be perceived as a method similar to local-search already used in EMAS (and many memetic algorithms). The obtained and presented in the end of the paper results show the applicability of this hybrid based on a selection of a number of 500 dimensional benchmark functions, when compared to non-hybrid, classic EMAS version.
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CITATION STYLE
Placzkiewicz, L., Sendera, M., Szlachta, A., Paciorek, M., Byrski, A., Kisiel-Dorohinicki, M., & Godzik, M. (2018). Hybrid Swarm and Agent-Based Evolutionary Optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10861 LNCS, pp. 89–102). Springer Verlag. https://doi.org/10.1007/978-3-319-93701-4_7
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