Abstract:

In order to improve the performance of particle swarm optimization (PSO) in complex constrained optimization problems, a hybrid method combining PSO and artificial bee colony (ABC) is proposed. A feasibility based rule is used to solve constrained problems, and the particle swarm is divided into feasible subpopulation and infeasible subpopulation. Some PSO particles containing the information of better feasible solutions and smaller constraint violation infeasible solutions are selected as food sources for ABC algorithm, which can make up for the tournament selection operator being invalid when the optimum is close to the boundary of constraint conditions. And the tabu table is used to save the local optimization results so as to avoid PSO trapping into local optimum. The algorithm is validated using four well studied benchmark problems, and the results indicate that the PSO-ABC algorithm can find out better optimum and has a stronger solidity.