An improved ensemble particle swarm optimizer using niching behavior and covariance matrix adapted retreat phase

Over the past two decades, to overcome the limitations of certain algorithms, ensemble strategies or self-adaptive mechanisms for evolutionary computation algorithms have been proposed. Regardless of how these strategies or mechanisms were designed, their objective was to control the balance between the global and local search capabilities during the evolutionary process. Inspired by this, a novel ensemble strategy with three groups to improve the performance of the ensemble particle swarm optimizer (EPSO) is proposed. The first group uses a covariance matrix adapted retreat phase (CMAR), the second group induces the niching behavior for inertia weight particle swarm optimization (PSO), and the third group maintains the characteristics of a large subpopulation of EPSO. Furthermore, a sample pool and replacement mechanism are proposed to perturb the three groups. This strategy also recommends a group of empirical allocation rates for subpopulations based on various proportion combination tests. The performance of the proposed strategy is evaluated using CEC2005 benchmark functions with 10, 30, and 50-dimensional tests and compared with those of the state-of-the-art PSO variants: EPSO, a modified PSO using adaptive strategy (MPSO), PSO variant for single-objective numerical optimization (PSO-sono), terminal crossover and steering-based PSO (TCSPSO), self-adapting hybrid strategy PSO (SaHSPS), and pyramid PSO (PPSO). Experimental results demonstrate that the improved EPSO, using CMAR, niching behavior, and sample pool, performs best.