Hybridizing Particle Swarm Optimization with JADE for continuous optimization

Abstract

As a population-based random search optimization technique, particle swarm optimization (PSO) has become an important branch of swarm intelligence (SI). To utilizing the advantage of operations in different SI, this study proposed a hybrid of multi-crossover operation and adaptive differential evolution with optional external archive (JADE), named PSOJADE, to balance the global and local search capabilities. In the experiments, the proposed algorithm is compared with six other advanced differential evolution (DE), PSO, and hybrid of DE and PSO techniques using 30 benchmark functions in CEC2017. To evaluate the effectiveness of the proposed PSOJADE more comprehensively, the experiments were implemented on 10-D, 30-D, and 50-D respectively. The experimental results indicate that the proposed algorithm yields better solution accuracy than the other techniques on 10-D, 30-D, and 50-D meanwhile.