模因算法
模因论
数学优化
初始化
计算机科学
水准点(测量)
局部搜索(优化)
人工智能
数学
机器学习
大地测量学
程序设计语言
地理
作者
Yangming Zhou,Jin‐Kao Hao,Béatrice Duval
标识
DOI:10.1109/tevc.2017.2674800
摘要
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.
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