Combining mutual information and stable matching strategy for dynamic evolutionary multi-objective optimization. Issue 9 (2nd September 2018)

• Record Type: Journal Article
• Authors: Fu, Xiaogang; Sun, Jianyong
• Is Part Of: Engineering optimization. Volume 50:Issue 9(2018)
• Page Start: 1434
• Page End: 1452
• Publication Date: 2018-09-02
• Subjects: Multi-objective evolutionary algorithm -- dynamic multiobjective optimization -- kinematics model -- mutual information -- stable matching strategy; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2017.1401066
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: It is reasonable to assume that the changing of the optimization environment is smooth when considering a dynamic multi-objective optimization problem. Learning techniques are widely used to explore the dependence structure to facilitate population re-initialization in evolutionary search paradigms. The aim of the learning techniques is to discover knowledge from history information, thereby to track the movement of the optimal front quickly through good initialization when a change occurs. In this article, a new learning strategy is proposed, where the main ideas are (1) to use mutual information to identify the relationship between previously found approximated solutions; (2) to use a stable matching mechanism strategy to associate previously found optimal solutions bijectively; and (3) to re-initialize the new population based on a kinematics model. Controlled experiments were carried out systematically on some widely used test problems. Comparison against several state-of-the-art dynamic multi-objective evolutionary algorithms showed comparable performance in favour of the developed algorithm.