Multi Algorithmic Approach to Galactic Swarm Optimization (MAGSO)

Abstract

Multi population algorithms have noted advantages over single population algorithms when optimizing over multimodal function space. The existence of multiple attractors allows exploration on multiple optimality regions which in turn provide more exploration compared with single population algorithms. This paper is a novel utilization of Galactic Swarm Optimization (GSO) framework in which the subpopulations are evolved with multiple variants of Particle Swarm Optimizers (PSOs) rather than single PSO. The proposed algorithm is called Multi Algorithmic Galactic Swarm Optimization (MAGSO). MAGSO utilizing multivariant PSOs in subswarm level combined with the unique superswarm tailored for exploitation has shown significant improvement of performance over GSO. Experiments done on Congress on Evolutionary Computation 2013 (CEC 2013) and other standard general benchmark suite indicate that the proposed MAGSO algorithm outperforms the previously proposed state-of-the-art swarm algorithms such as Bollinger bands approach on boosting ABC (ABCBB) and Dynamic Neighbourhood Learning Particle Swarm Optimizer (DNLPSO) besides GSO. Detailed statistical evidence over 51 independent trials on the benchmark functions indicate the best performance of MAGSO. The main research goal is to propose a superior performing algorithm by exploiting the GSO framework. In order to show its efficacy, the algorithm is compared with state-of-the-art swarm algorithms such as ABCBB, DNLPSO and unmodified GSO. The method proposed here is quite general and demonstrates that more ability and extension work possible from the basic GSO framework.