Global Prediction-Based Adaptive Mutation Particle Swarm Optimization

Particle swarm optimization (PSO) algorithm has attracted great attention as a stochastic optimizing method due to its simplicity and power strength in optimization fields. However, two issues are still to be improved, especially, for complex multimodal problems. One is the premature convergence for multimodal problems. The other is the low efficiency for complex problems. To address these two issues, firstly, a strategy based on the global optimum prediction is proposed. A predicting model is established on the low-dimensional feature space with the principle component analysis technique, which has the ability to predict the global optimal position by the feature reflecting the evolution tendency of the current swarm. Then the predicted position is used as a guideline exemplar of the evolution process together with pbest and gbest. Secondly, a strategy, called adaptive mutation, is proposed, which can evaluate the crowding level of the aggregating particle swarm by using the distribution topology of each dimension, and hence, can get the possible location of local optimums and escape from the valleys with the generalized non-uniform mutation operator subsequently. The performance of the proposed global prediction-based adaptive mutation particle swarm optimization (GPAM-PSO) is tested on 8 well-known benchmark problems, compared with 9 existing PSO in terms of both accuracy and efficiency. The experimental results demonstrate that GPAM-PSO outperforms all reference PSO algorithms on both the solution quality and convergence speed.