An improved particle swarm optimization algorithm and its application to solve constrained optimization problems
Rui Chi, Xuexin Chi · 2020
This paper presents an improved particle swarm optimization algorithm (IPSO) for constrained nonlinear optimization problems. The main improvements of the original PSO are the incorporation of particle local information and the introduction of dynamic inertia weight. A new velocity update equation is proposed, which consider the local information around each particle. During consecutive generations, an dynamic inertia weight is produced to improve the search speed and search precision of IPSO. In addition, a dynamic multi-stage penalty function is used to deal with the constraints and a modified feasibility-based rule is adopted to update the personal best position and the global best position of the IPSO. The effectiveness of the proposed IPSO is validated through a set of benchmark constrained test functions and an engineering optimization problem. The experimental results show that IPSO can obtain some solutions better than or as well as those previously reported in the literature, which reveals that the proposed IPSO is a promising approach to solve this sort of optimizing problems.