The Enhanced Vector of Convergence for Particle Swarm Optimization based on constrict factor

The Particle Swarm Optimizer is used very widely for unimodal and multi-modal optimization problems. Recently, most of variant PSOs are combing several evolutionary strategies in order to achieve a better performance on Benchmark functions, and even for shifted, rotated, or composite functions. In this paper, a new method known as Enhanced Vector of Convergence is proposed and combined with constrict factor to improve the convergence performance of Particle Swarm Optimizer. In experimental study, other 5 variant Particle Swarm Optimizers are compared, and acceptance rate, t-Test are used for further evaluation. The results indicate that the Enhance Vector of Convergence can significantly improve the accurate level of Particle Swarm Optimizer.