A New Hybrid Particle Swarm Optimization with Variable Neighborhood Search for Solving Unconstrained Global Optimization Problems

Over the past few decades, metaheuristics have been emerged to combine basic heuristic techniques in higher level frameworks to explore a search space in an efficient and an effective way. Particle swarm optimization (PSO) is one of the most important method in metaheuristics methods, which is used for solving unconstrained global optimization prblems. In this paper, a new hybrid PSO algorithm is combined with variable neighborhood search (VNS) algorithm in order to search for the global optimal solutions for unconstrained global optimization problems. The proposed algorithm is called a hybrid particle swarm optimization with a variable neighborhood search algorithm (HPSOVNS). HPSOVNS aims to combine the PSO algorithm with its capability of making wide exploration and deep exploitation and the VNS algorithm as a local search algorithm to refine the overall best solution found so far in each iteration. In order to evaluate the performance of HPSOVNS, we compare its performance on nine different kinds of test benchmark functions with four particle swarm optimization based algorithms with different varieties. The results show that HPSOVNS algorithm achieves better performance and faster than the other algorithms.