Hybridizing Particle Swarm Optimization with JADE for continuous optimization

• Published in: Multimedia tools and applications Vol. 79; no. 7-8; pp. 4619 - 4636
• Main Authors: Du, Sheng-Yong, Liu, Zhao-Guang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2020; Springer Nature B.V
• Subjects: Algorithms; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Evolutionary computation; Multimedia Information Systems; Optimization techniques; Particle swarm optimization; Special Purpose and Application-Based Systems; Swarm intelligence; Differential evolution; Continuous optimization; Particle swarm optimization; Hybrid algorithm
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-08142-7

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.