Journal Article

A dynamic multiobjective evolutionary algorithm based on fine prediction strategy and nondominated solutions-guided evolution

Ma, Yongjie, Wang, Peidi

2023

Overview

The dynamic multiobjective evolutionary algorithm (DMOEA) is an efficient solver for dynamic multiobjective optimization problems (DMOPs). It is challenging for algorithms to converge quickly and maintain diversity in new environments. However, existing DMOEAs incorporate strategies only in the environment response stage, which may limit the further improvement of the algorithm performance. To address this problem, different strategies have been proposed in the environment response stage and static optimization stage to balance convergence and diversity throughout the optimization process. In the static optimization stage, nondominated solutions-guided evolution leaves the individuals that are closer to the nondominated individuals among the original individuals and the opposition individuals generated by opposition-based learning, which can accelerate the convergence of each generation. In the environment response stage, based on the individual dominance relationship before environmental change, the fine prediction strategy performs difference prediction and opposition-based learning prediction for nondominated and dominated individuals, respectively, which results in an initial population with good convergence and diversity in the new environment. The performance of proposed algorithm was evaluated on 22 instances and compared to eight state-of-the-art algorithms. The results show that proposed algorithm outperforms its competitors on most problems. Additionally, proposed algorithm performs better in early environmental changes and is relatively insensitive to different severity changes.

• Publisher: Springer Nature B.V
• Subject: Convergence / Evolutionary algorithms / Genetic algorithms / Learning / Multiple objective analysis / Optimization