Hybridizing Particle Swarm Optimization with Invasive Weed Optimization for Solving Nonlinear Constrained Optimization Problems

摘要

Most of engineering applications are occurring in the form of nonlinear constrained optimization problems. They have to be solved in point of accuracy and faster convergence. In this paper, the combination of particle swarm optimization (PSO) and invasive weed optimization (IWO) is discussed and the stochastic ranking method is incorporated to handle the constraints, named as a PSO-IWO-SR. Due to page limitation, four well-known nonlinear constrained optimization engineering design problems are adopted to validate the performance of the PSO-IWO-SR. The results obtained by the proposed method PSO-IWO-SR are better than the stateof- the-art evolutionary algorithms with respect to accuracy and computational time.