Competitive swarm optimizer with dynamic multi-competitions and convergence accelerator for large-scale optimization problems

Large-scale optimizations (LSOPs) with high dimensional decision variables have become one of the most challenging problems in engineering optimization. High dimensional information causes serious interference to the algorithm optimization performance. The optimization performance of the algorithms will be seriously degraded. Competitive swarm optimizer (CSO) is a robust algorithm to tackle LSOPs. However, CSO randomly selects two particles to compare, then generates the winner and the loser. Although this search mechanism can enhance the diversity of the swarm, a single comparison is difficult to guarantee the quality of winners and losers. Therefore, there exists a risk of producing unqualified solutions. In order to enhance the quality of solution, a novel CSO with dynamic multi-competitions and convergence accelerator, namely DMCACSO, is designed in this paper. In the DMCACSO, a dynamic multi-competitions based evolutionary information is designed to pick out the losers more efficiently and improve the quality of winners. In addition, a convergence accelerator with hybrid evolutionary strategy is developed to speed up the particle search when the algorithm is a state of stagnation. The experiment results in solving large-scale benchmark functions from CEC2010 and CEC2013 indicate that the DMCACSO has competitive optimization performance by comparing with some state-of-the-art algorithms. Finally, the DMCACSO is effective in terms of quality in solving an actual feature selection problem.