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

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Abstract

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.

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  1. University of Trieste, Piazzale Europa 1, 34127, Trieste, Italy

    Massimiliano Kaucic

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Kaucic, M. A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization. J Glob Optim 55, 165–188 (2013). https://doi.org/10.1007/s10898-012-9913-4

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