QUAntum Particle Swarm Optimization: an auto-adaptive PSO for local and global optimization

• Published in: Computational optimization and applications Vol. 82; no. 2; pp. 525 - 559
• Main Authors: Flori, Arnaud, Oulhadj, Hamouche, Siarry, Patrick
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2022; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Computer Science; Convex and Discrete Geometry; Evolutionary computation; Global optimization; Heuristic methods; Management Science; Mathematics; Mathematics and Statistics; Operations Research; Operations Research/Decision Theory; Optimization; Parameters; Particle swarm optimization; Quantum mechanics; Simulated annealing; Statistics; Swarm intelligence; Swarm intelligence algorithm; Auto-adaptive algorithm; Particle Swarm Optimization; Self-organization
• ISSN: 0926-6003; 1573-2894
• DOI: 10.1007/s10589-022-00362-2

Particle Swarm Optimization (PSO) is a population-based metaheuristic belonging to the class of Swarm Intelligence (SI) algorithms. Nowadays, its effectiveness on many hard problems is no longer to be proven. Nevertheless, it is known to be strongly sensitive on the choice of its settings and weak for local search. In this paper, we propose a new algorithm, called QUAntum Particle Swarm Optimization (QUAPSO) based on quantum superposition to set the velocity PSO parameters, simplifying the settings of the algorithm. Another improvement, inspired by Kangaroo Algorithm (KA), was added to PSO in order to optimize its efficiency in local search. QUAPSO was compared with a set of six well-known algorithms from the literature (two parameter sets of classical PSO, KA, Differential Evolution, Simulated Annealing Particle Swarm Optimization, Bat Algorithm and Simulated Annealing Gaussian Bat Algorithm). The experimental results show that QUAPSO outperforms the competing algorithms on a set of 30 test functions.