%0 Journal Article %T Opposition-based Memetic Search for the Maximum Diversity Problem %+ Laboratoire d'Etudes et de Recherche en Informatique d'Angers (LERIA) %+ Institut Universitaire de France %A Zhou, Yangming %A Hao, Jin-Kao %A Duval, Béatrice %< avec comité de lecture %@ 1089-778X %J IEEE Transactions on Evolutionary Computation %I Institute of Electrical and Electronics Engineers %V 21 %N 5 %P 731-745 %8 2017 %D 2017 %R 10.1109/TEVC.2017.2674800 %K Learning-based optimization %K Maximum diversity %K Memetic search %K opposition-based learning %K tabu search %Z Computer Science [cs]/Artificial Intelligence [cs.AI]Journal articles %X
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 wellknown 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 opposition-based learning for solving MDP.
%G English %2 https://univ-angers.hal.science/hal-02709499/document %2 https://univ-angers.hal.science/hal-02709499/file/zhouduvalhaoieee2017.pdf %L hal-02709499 %U https://univ-angers.hal.science/hal-02709499 %~ UNIV-ANGERS %~ LERIA