A Novel Prediction Strategy Based on Change Degree of Decision Variables for Dynamic Multi-Objective Optimization

Abstract

Effectively balancing the convergence and diversity in dynamic environments is a challenging task. In order to handle the issue, this paper proposes a novel prediction strategy based on change degree of decision variables for dynamic multi-objective optimization (CDDV), which has the ability to detect the change degree in the decision space and design the different prediction strategy to make the population adapt to the new environment. The proposed method can adaptively increase population diversity according to the analysis of change degree, when an environmental change is detected. In order to study the efficacy and usefulness of the novel change degree on evolutionary algorithms, a range of dynamic multi-objective benchmark problems are selected to evaluate the performance of the proposed algorithm. The results demonstrate the effectiveness of proposed algorithm in compared with four other state-of-the-art methods.

Keywords

• Dynamic multi-objective optimization
• evolutionary algorithms
• change degree
• diversity