Particle swarm optimizer with aging operator for multimodal function optimization

This paper proposes a new scheme for preventing a Particle Swarm Optimizer from premature convergence on multimodal optimization problems. Instead of only using fitness evaluation, we use a new index called particle age to guide population towards more promising region of the search space. The particle age is a measure of how long each particle moves towards a better solution. The main novelty of the proposed method is to let each particle learn from not only neighbours with better fitness values but also the neighbours whose fitness values are updated more frequently. To achieve this, we design a comprehensive age-based learning strategy, in which age is used for excluding old particles, selecting learning exemplars and deciding mutation strength and inertial weight for each particle. Experiments were conducted on 15 multimodal test functions to assess the performance of this new strategy in comparison with 7 state-of-the-art PSOs from the literature. The experimental results show the good performance of the proposed algorithm in solving multimodal functions when compared with several existing PSO variants.