Evolutionary dynamic optimization: A survey of the state of the art
Evolutionary dynamic optimization: A survey of the state of the art
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DOI：
10.1016/j.swevo.2012.05.001
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
Trung-Thanh Nguyen;Shengxiang Yang;J. Branke
中科院分区：
文献类型：
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作者：
Trung-Thanh Nguyen;Shengxiang Yang;J. Branke
动态环境中的优化是一项具有挑战性但重要的任务，因为许多现实世界的优化问题都随着时间的推移而变化。进化计算和群体智能是解决动态环境中优化问题的好工具，因为它们的灵感来自自然自组织系统和生物进化，这些系统总是受到不断变化的环境的影响。动态环境下的进化优化（Evolutionary Optimization in Dynamic Environments，简称EDO）是近20年来进化计算领域中最活跃的研究方向之一。在本文中，我们进行了深入调查的国家的最先进的学术研究领域的EDO和其他元算法在四个领域：基准问题/发电机，性能指标，算法方法和理论研究。其目的是第一次（i）详细解释目前的方法如何工作;（ii）审查每种方法的优点和缺点;（iii）讨论现有EDO研究的当前假设和覆盖范围;（iv）确定EDO当前的差距，挑战和机遇。
Optimization in dynamic environments is a challenging but important task since many real-world optimization problems are changing over time. Evolutionary computation and swarm intelligence are good tools to address optimization problems in dynamic environments due to their inspiration from natural self-organized systems and biological evolution, which have always been subject to changing environments. Evolutionary optimization in dynamic environments, or evolutionary dynamic optimization (EDO), has attracted a lot of research effort during the last 20 years, and has become one of the most active research areas in the field of evolutionary computation. In this paper we carry out an in-depth survey of the state-of-the-art of academic research in the field of EDO and other meta-heuristics in four areas: benchmark problems/generators, performance measures, algorithmic approaches, and theoretical studies. The purpose is to for the first time (i) provide detailed explanations of how current approaches work; (ii) review the strengths and weaknesses of each approach; (iii) discuss the current assumptions and coverage of existing EDO research; and (iv) identify current gaps, challenges and opportunities in EDO.