Composable Role-Based Diversity Control for Particle Swarm Optimization
Particle Swarm Optimization (PSO) remains highly effective on many continuous optimization problems, yet its search dynamics collapse too quickly on high-dimensional and deceptive landscapes. Our earlier work introduced single-role diversity-enhancing PSO variants and showed that diversity injection is effective only when embedded in swarm dynamics in a structurally meaningful way. The present study extends those previously published single-role variant results. It introduces a unified role-based framework for informed diversity control. The main benefit, we show, comes not from isolated operators, but from composing behavioral roles across the social and cognitive velocity components. The framework organizes diversity mechanisms into three families: repulsion from best solutions, attraction toward worst solutions, and repulsion from worst solutions. Beyond single-role variants, we introduce paired-role formulations and three hybrid architectures allowing different roles to act on different velocity components within the same swarm. Empirical evaluation on 32 benchmark functions across dimensionalities up to 1000 reveals that these multi-hybrid strategies consistently outperform standard PSO and remain highly competitive with state-of-the-art algorithms such as CMA-ES and L-SHADE in high-dimensional spaces.