Investigation of Deterministic Particle Swarm Optimization with Periodic Function
In this study, we propose the deterministic particle swarm optimization introducing periodic function (DPSOP). The particle swarm optimization (PSO) is one of evolutional algorithm and is constructed by many agents. The agents have a position and velocity that are updated according to the global best and the local best. The amount of agent moving is generally decided randomly. In the proposed DPSOP, the amount of agent moving is decided sine and cosine wave. Each agent is obtained different phase, and the amount of agent moving to the global best and the local best is changed according to cosine and sine wave, respectively. The moving for global best is out of phase with the local best by pi/2. Depending on the phase difference, the agent's movement will be divided into a time when it is maximally close to the global best and a time when it is maximally close to the local best. Thereby, the DPSOP is able to perform efficient solution search. We confirm that the performance of the proposed DPSOP by using searching solution of five benchmark functions.
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