Opposition-Based Memetic Search for the Maximum Diversity Problem

• Published in: IEEE transactions on evolutionary computation Vol. 21; no. 5; pp. 731 - 745
• Main Authors: Yangming Zhou, Jin-Kao Hao, Duval, Beatrice
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Artificial Intelligence; Computation; Computer Science; Data mining; Heuristic algorithms; Learning-based optimization; Local optimization; Lower bounds; Machine learning; maximum diversity; memetic search; Memetics; opposition-based learning (OBL); Optimization; Search problems; Search process; Sociology; Statistics; tabu search (TS); tabu search; Learning-based optimization; opposition-based learning; Maximum diversity; Memetic search
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2017.2674800

As a usual model for a variety of practical applications, the maximum diversity problem (MDP) is computational challenging. In this paper, we present an opposition-based memetic algorithm (OBMA) for solving MDP, which integrates the concept of opposition-based learning (OBL) into the well-known memetic search framework. OBMA explores both candidate solutions and their opposite solutions during its initialization and evolution processes. Combined with a powerful local optimization procedure and a rank-based quality-and-distance pool updating strategy, OBMA establishes a suitable balance between exploration and exploitation of its search process. Computational results on 80 popular MDP benchmark instances show that the proposed algorithm matches the best-known solutions for most of instances, and finds improved best solutions (new lower bounds) for 22 instances. We provide experimental evidences to highlight the beneficial effect of OBL for solving MDP.