An adaptive two-layer particle swarm optimization with elitist learning strategy

This study presents an adaptive two-layer particle swarm optimization algorithm with elitist learning strategy (ATLPSO-ELS), which has better search capability than classical particle swarm optimization. In ATLPSO-ELS, we perform evolution on both the current swarm and the memory swarm, motivated by the tendency of the latter swarm to distribute around the problem's optima. To achieve better control of exploration/exploitation searches in both current and memory swarms, we propose two adaptive division of labor modules to self-adaptively divide the swarms into exploration and exploitation sections. In addition, based on the orthogonal experimental design and stochastic perturbation techniques, an elitist learning strategy module is introduced in the proposed algorithm to enhance the search efficiency of swarms and to mitigate premature convergence. A comprehensive experimental study is conducted on a set of benchmark functions. Compared with various state-of-the-art PSOs and metaheuristic search variants, ATLPSO-ELS performs more competitively in the majority of the benchmark functions. (C) 2014 Elsevier Inc. All rights reserved.