Dynamic and Adjustable Particle Swarm Optimization
Chen-yi Liao, Wei‐Ping Lee, Xianghan Chen, Cheng-wen Chiang · 2013
Abstract:- Particle Swarm Optimization (PSO) is a stochastic, population-based evolutionary search technique. It has difficulties in controlling the balance between exploration and exploitation. In order to improve the performance of PSO and maintain the diversities of particles, we propose a novel algorithm called Dynamic and Adjustable Particle Swarm Optimization (DAPSO). The distance from each particle to the global best position is calculated in order to adjust the velocity suitably of each particle. Four benchmark functions such as Sphere, Rosenbrock, Rastrigrin, Griewank are used for the comparison of DAPSO with the Standard PSO. The experiments prove that DAPSO has better performance than the Standard PSO.