A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization

• Published in: Journal of global optimization Vol. 55; no. 1; pp. 165 - 188
• Main Author: Kaucic, Massimiliano
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.01.2013; Springer; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Analysis; Computer Science; Convergence; Genetic algorithms; Mathematical optimization; Mathematics; Mathematics and Statistics; Mutation; Neighborhoods; Operations Research/Decision Theory; Optimization; Optimization algorithms; Real Functions; Robustness; Stagnation; Strategy; Swarm intelligence; Velocity; Hybrid methods; Restart techniques; Opposition-based computing; Bound constrained optimization; Particle swarm optimization
• ISSN: 0925-5001; 1573-2916
• DOI: 10.1007/s10898-012-9913-4

In this paper we present a multi-start particle swarm optimization algorithm for the global optimization of a function subject to bound constraints. The procedure consists of three main steps. In the initialization phase, an opposition learning strategy is performed to improve the search efficiency. Then a variant of the adaptive velocity based on the differential operator enhances the optimization ability of the particles. Finally, a re-initialization strategy based on two diversity measures for the swarm is act in order to avoid premature convergence and stagnation. The strategy uses the super-opposition paradigm to re-initialize particles in the swarm. The algorithm has been evaluated on a set of 100 global optimization test problems. Comparisons with other global optimization methods show the robustness and effectiveness of the proposed algorithm.