Switching Between Swarm Optimization Algorithms During a Run: An Empirical Study

Abstract

In this paper, it is proposed to switch between swarm optimization algorithms during the optimization process, aiming at combining the best performance from each single swarm optimization algorithm and hence improving the overall empirical performance. To investigate this switching scheme, an empirical study is carried on four swarm algorithms, Particle Swarm Optimization, Gravitational Search, Grey Wolf Optimizer and Chicken Swarm Optimization. All possible switches (combinations) among those four algorithms are tested. In addition, instead of searching for the best switching point, it is prescribed in the experiment. The proposed method is empirically investigated on the so-called Black-Box Optimization Benchmark (BBOB), where the switching method is compared to each individual swarm algorithm as well as using the state-of-the-art black-box optimization algorithm, BIPOP-CMAES (BI-population Covariance Matrix Adaptation Evolution Strategy). The result shows that the switching approach generally improves the performance of individual swarm algorithms. However, none of them can rival the performance of the BIPOP-CMA-ES.