An adaptive differential evolution algorithm based on belief space and generalized opposition-based learning for resource allocation

Abstract

Differential evolution (DE) algorithm is prone to premature convergence and local optimization in solving complex optimization problems. In order to solve these problems, the belief space strategy, generalized opposition-based learning strategy and parameter adaptive strategy are introduced into DE to propose an improved adaptive DE algorithm, namely ACDE/F in this paper. In the ACDE/F, the idea of cultural algorithm and different mutation strategies are introduced into belief space to balance the global exploration ability and local optimization ability. A generalized opposition-based learning strategy is designed to improve the convergence speed of local optimization and increase the population diversity. A parameter adaptive adjustment strategy is developed to reasonably adjust the mutation factor and crossover factor to avoid to fall into local optimum. In order to test and verify the optimization performance of the ACDE/F, the unimodal functions and multimodal functions from CEC 2005 and CEC 2017 are selected in here. The experiment results show that the ACDE/F has better optimization performance than the DE with different strategies, WMSDE, DE2/F, GOAL-RNADE and DE/best/1. In addition, the actual gate allocation problem is selected to verify the practical application ability of the ACDE/F. The ACDE/F obtains the maximum allocation rate and average allocation rate of 98% and 96.8%, respectively. Therefore, the experimental results show that the ACDE/F can effectively solve the gate allocation problem and obtain ideal gate allocation results.