Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
EPSO - best-of-two-worlds meta-heuristic applied to power system problems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Enhancing MOEA/D with guided mutation and priority update for multi-objective optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A hybrid evolutionary approach with search strategy adaptation for mutiobjective optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Evolution strategy (ES) and particle swarm optimization (PSO) are two of the most popular research topics for tackling real-parameter optimization problems in evolutionary computation. Both of them have strengths and weaknesses for their different search behaviors and methodologies. In ES, mutation, as the main operator, tries to find good solutions around each individual. While in PSO, particles are moving toward directions determined by certain global information, such as the global best particle. In order to leverage the specialties offered by both sides to our advantage, this paper combines the essential mechanism of ES and the key concept of PSO to develop a new hybrid optimization methodology, called particle swarm guided evolution strategy. We introduce swarm intelligence to the ES mutation framework to create a new mutation operator, called guided mutation, and integrate the guided mutation operator into ES. Numerical experiments are conducted on a set of benchmark functions, and the experimental results indicate that PSGES is a promising optimization methodology as well as an interesting research direction.