Dynamic Multiobjective Optimization Algorithm Guided by Recurrent Neural Network

In recent years, prediction-based algorithms have attracted much attention for solving dynamic multiobjective optimization (DMO) problems in the evolutionary computing community. However, this class of algorithms still has potential for further improvements by enhancing the historical information extraction approach to balance convergence and diversity. In this article, we propose a DMO algorithm based on a recurrent neural network (RNN) to balance the population’s convergence and diversity in dynamic environments. The RNN 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 DMO problems than several recent algorithms.