The comparison of different PDP-type self-adaptive schemes for the cooperation of GA, DE, and PSO algorithms

Abstract

Many global optimization problems are presented as a black-box model, in which there is no information on the objective function properties. Traditional optimization algorithms usually can't effectively solve that kind of problems. Different heuristics and metaheuristics are usually applied in that case. Evolutionary algorithms are one of the most popular and effective approaches to black-box optimization problems. However, it's hard to choose one specific method that will solve the given problem better than other algorithms. For dealing with this issue, self-adaptive schemes are usually implemented. In this paper we have investigated the performance of different PDP-type adaptive schemes using such popular evolutionary-based algorithms as Genetic Algorithm, Differential Evolution, and Particle Swarm Optimization. The experimental results on a set of benchmark problems have shown that investigated schemes can improve the performance compared with the performance of a stand-alone evolutionary algorithm. At the same time the choice of a scheme and its parameters affect the results.