A hybrid immune PSO for constrained optimization problems

Precise Algorithms combining evolutionary algorithms and constraint-handling techniques have shown to be effective to solve constrained optimization problems during the past decade. This paper presents a hybrid immune PSO (HIA-PSO) algorithm with a feasibility-based rule which is employed in this paper to handle constraints in solving global nonlinear constrained optimization problems, and Nelder-Mead simplex search method is used to improve the performance of local search in the algorithm. Simulation results indicate that HIA-PSO approach is an efficient method to improve the performance of immune PSO (IA-PSO) in searching ability to global optimum. The proposed HIA-PSO approach performed consistently well on the studies of Benchmark functions, with better results than previously published solutions for these problems.