Dynamic Multi-Objective Optimization Algorithm Guided by Recurrent Neural Network

Abstract

In recent years, prediction-based algorithms have attracted much attention for solving dynamic multi-objective optimization problems in the evolutionary computing community. However, this class of algorithms still has potential for further improvements by enhaneing the historical information extraction approach to balance convergence and diversity. In this paper, we propose a dynamic multi-objective optimization algorithm based on a recurrent neural network to balance the population’s convergence and diversity in dynamic environments. The recurrent neural network model in the proposed algorithm employs online learning in order to constantly improve according to the increasing evolutionary information. Meanwhile, differing from most existing prediction-based algorithms, the learning machine is not limited by assumptions, such as linear or nonlinear correlation, when it predicts new solutions for future evolutionary environments. Besides, an auxiliary strategy is performed, which adaptively introduces the random or mutated solutions according to the error losses between the prediction solutions and the optimal solutions in the whole optimization process. The experimental results show that the proposed algorithm is more effective for handling dynamic multi-objective optimization problems than several recent algorithms.