Multi-strategy Genetic Algorithm for Self-Configuring Solving of Complex Optimization Problems
Evgenii A. Sopov · 2015
Many complex optimization problems require a modification of the general evolutionary algorithm (EA) according to the given features of the problem. There exist a great variety of EAs that represent different search strategies for many classes and subclasses of optimization problems. Real-world problems may combine several features that are not known beforehand, thus there is no information about what EA to choose and what EA's settings to apply for efficient problem solving. This study presents a novel metaheuristic for designing multi-strategy EA based on the hybrid of the island model, cooperative and competitive co evolution schemes. The approach controls interactions of EAs and leads to the self-configuring solving of problems with a priori unknown structure. Two examples of implementations of the approach for multi-objective and non-stationary optimization are discussed. The results of numerical experiments for benchmark problems from CEC competitions are presented. The proposed approach has demonstrated the efficiency comparable with other well-studied techniques for multi-objective and non-stationary optimization. And it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.