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Opposition-based Memetic Search for the Maximum Diversity Problem

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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.

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https://hal.univ-angers.fr/hal-02709499
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Dernière modification le : mercredi 3 juin 2020 - 03:58:00
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Yangming Zhou, Jin-Kao Hao, Béatrice Duval. Opposition-based Memetic Search for the Maximum Diversity Problem. IEEE Transactions on Evolutionary Computation, Institute of Electrical and Electronics Engineers, 2017, 21 (5), pp.731-745. ⟨10.1109/TEVC.2017.2674800⟩. ⟨hal-02709499⟩

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