Hybrid Swarm and Agent-Based Evolutionary Optimization
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.
The research presented in this paper was partially supported by the Grant of the Dean of Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, for Ph.D. Students.
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