A Hybrid Group Search Optimizer with Opposition-Based Learning and Differential Evolution

Abstract

Group search optimizer (GSO) is a recently developed heuristic inspired by biological group search resources behavior. However, it still has some defects such as slow convergence speed and poor accuracy of solution. In order to improve the performance of GSO in solving complex optimization problems, an opposition-based learning approach (OBL) and a differential evolution method (DE) are integrated into GSO to form a hybrid GSO. In this paper, the strategy of OBL is used to enlarge the search region, and the operator of DE is utilized to enhance local search to improve. Comparison experiments have demonstrated that our hybrid GSO algorithm performed advantages over previous GSO and DE approaches in convergence speed and accuracy of solution.