An Improved Particle Swarm Optimization with Feasibility-Based Rules for Constrained Optimization Problems

This paper presents an improved particle swarm optimization (IPSO) to solve constrained optimization problems, which handles constraints based on certain feasibility-based rules. A turbulence operator is incorporated into IPSO algorithm to overcome the premature convergence. At the same time, a set called FPS is proposed to save those P-best locating in the feasible region. Different from the standard PSO, g(best) in IPSO is chosen from the FPS instead of the swarm. Furthermore, the mutation operation is applied to the P-best with the maximal constraint violation value in the swarm, which can guide particles to close the feasible region quickly. The performance of IPSO algorithm is tested on a well-known benchmark suite and the experimental results show that the proposed approach is highly competitive, effective and efficient.