An intelligent augmentation of particle swarm optimization with multiple adaptive methods

Over the last two decades, the newly developed optimization technique – Particle Swarm Optimization (PSO) has attracted great attention. Two common criticisms exist. First, most existing PSOs are designed for a specific search space thus an algorithm performing well on a diverse set of problems is lacking. Secondly, PSO suffers premature convergence. To address the first issue, we propose to augment PSO via the fusion of multiple search methods. An intelligent selection mechanism is developed based on an effectiveness index to trigger appropriate search methods. In this research, two search techniques are studied: a non-uniform mutation-based method and an adaptive sub-gradient method. We further improve the proposed PSO using adaptive Cauchy mutation to prevent premature convergence. As a result, an augmented PSO with multiple adaptive methods (PSO-MAM) is proposed. The performance of PSO-MAM is tested on 43 functions (uni-modal, multi-modal, non-separable, shifted, rotated, noisy and mis-scaled functions). The results are compared in terms of solution quality and convergence speed with 10 published PSO methods. The experimental results demonstrate PSO-MAM outperforms the comparison algorithms on 36 out of 43 functions. We conclude, while promising, there is still room for improving PSO-MAM on complex multi-modal functions (e.g., rotated multi-modal functions).