Particle swarm optimization with age-group topology for multimodal functions and data clustering

• We proposes a new swarm topology based on age to decide neighborhoods for particles.
• We presents a novel parameter adaptation strategy for particle swarm optimization.
• The proposed algorithm is compared with 4 PSOs on 12 multimodal functions.
• The proposed algorithm is compared with the four evolutionary-algorithm-based clustering methods on 7 real-life datasets.

This paper proposes particle swarm optimization with age-group topology (PSOAG), a novel age-based particle swarm optimization (PSO). In this work, we present a new concept of age to measure the search ability of each particle in local area. To keep population diversity during searching, we separate particles to different age-groups by their age and particles in each age-group can only select the ones in younger groups or their own groups as their neighbourhoods. To allow search escape from local optima, the aging particles are regularly replaced by new and randomly generated ones. In addition, we design an age-group based parameter setting method, where particles in different age-groups have different parameters, to accelerate convergence. This algorithm is applied to nonlinear function optimization and data clustering problems for performance evaluation. In comparison against several PSO variants and other EAs, we find that the proposed algorithm provides significantly better performances on both the function optimization problems and the data clustering tasks.