Improved Directionally Driven Self-regulating Particle Swarm Optimizer
Particle Swarm Optimization (PSO) and many of its variants tend to suffer from premature convergence on strongly multimodal test problems. It is known that maintenance of diversity in the swarm is very important for preventing the swarm from converging prematurely for multimodal function problems. In this paper, an improved version of the Directionally Driven Self-Regulating Particle Swarm Optimization (DD-SRPSO) algorithm is proposed with a mechanism to maintain diversity without incurring any significant computational cost referred to as Improved DD-SRPSO. An attractive and repulsive swarm update strategy inspired from ARPSO is incorporated in DD-SRPSO. The proposed method is tested using the shifted, rotated and complex benchmark functions from CEC2013. These results are compared with eight PSO variants including DD-SRPSO. The results indicate that the proposed method improves results on some of the benchmark functions and gives comparable results for others.
KeywordsParticle swarm optimization Directional update strategy Rotational invariant strategy Diversity guided search
The author thanks Prof. Suresh Sundaram and Dr. Senthilnath J. for the help and guidance provided by them.
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