A cooperative approach for combining particle swarm optimization and differential evolution algorithms to solve single-objective optimization problems

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 52; no. 4; pp. 4089 - 4108
• Main Authors: Dadvar, Marziyeh, Navidi, Hamidreza, Javadi, Hamid Haj Seyyed, Mirzarezaee, Mitra
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2022; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Computer Science; Evolutionary algorithms; Evolutionary computation; Game theory; Machines; Manufacturing; Mechanical Engineering; Optimization; Particle swarm optimization; Processes; Differential evolution; Particle swarm optimization; Cooperative game theory; Nash bargaining theory
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-021-02605-x

The present paper proposes a new algorithm designed for solving optimization problems. This algorithm is a hybrid of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. The proposed algorithm uses a coalition or cooperation model in the game theory to combine the DE and PSO algorithms. This is done in an attempt to keep a balance between the exploration and exploitation capabilities by preventing population stagnation and avoiding the local optimum. The DE and PSO algorithms are two players in the state space, which play cooperative games together using the Nash bargaining theory to find the best solution. To evaluate the performance of the proposed algorithm, 25 benchmark functions are used in terms of the CEC2005 structure. The proposed algorithm is then compared with the classical DE and PSO algorithms and the hybrid algorithms recently proposed. The results indicated that the proposed hybrid algorithm outperformed the classical algorithms and other hybrid models.