An Adaptive Multi-Strategy Algorithm Based on Extent of Environmental Change for Dynamic Multiobjective Optimization

The most obvious characteristic of dynamic multi-objective optimization problems (DMOPs) is the time-varying Pareto-optimal set (POS) or/and Pareto-optimal front (POF). This kind of problem poses a higher challenge to evolutionary algorithms, as it requires populations to rapidly track and converge the updated POF in new environments. Differing from the superposition of several strategies in the literatures, we propose an adaptive multi-strategy algorithm based on the extent of environmental change, called AMEEC, to effectively handle various dynamic changes. AMEEC chooses the corresponding strategies for different environmental changes adaptively. When the environment changes moderately or similarly, the prediction based on clustered center points, POS manifold prediction, and generation of random solutions based on ideal points are employed to relocate the population individuals in the new environment. Otherwise, the trend prediction model is employed to predict the knee points of each part and the center points of each cluster, and to adaptively adjust the area of random solutions based on ideal and nadir points focuses on enhancing the diversity of population members. The proposed AMEEC is tested comprehensively on nineteen benchmark problems compared with six state-of-the-art algorithms. All algorithms use RM-MEDA (a regularity model-based multi-objective estimation of distribution algorithm) as a static optimizer. The experimental results demonstrate that AMEEC can achieve good convergence, diversity, and distribution, and is more competitive in dealing with dynamic problems.