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

作者：Kaucic Massimiliano^*

来源：Journal of Global Optimization, 2013, 55(1): 165-188.

DOI：10.1007/s10898-012-9913-4

• Particle swarm optimization
• Restart techniques
• Opposition-based computing
• Hybrid methods
• Bound constrained optimization

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.

• 出版日期2013-1