Evolutionary programming with ensemble of explicit memories for dynamic optimization
Evolutionary programming with ensemble of explicit memories for dynamic optimization
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DOI：
10.1109/cec.2009.4982978
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发表时间：
2009-05
期刊：
影响因子：
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通讯作者：
Ling Yu;P. Suganthan
中科院分区：
文献类型：
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作者：
Ling Yu;P. Suganthan
本文提出了一种具有集成记忆的进化规划方法来处理动态环境中的优化问题。该算法通过引入模拟退火的动态策略参数，以及应用局部搜索对最改善的方向修改了最新版本的进化规划。作为短期和长期记忆的外部档案的集合增强了人口的多样性。当环境发生变化时，档案成员也是基本的解决方案。在CEC 2009年动态和不确定环境下进化计算竞赛提供的6个多峰问题上测试了该算法，并给出了结果。
This paper presents the evolutionary programming with an ensemble of memories to deal with optimization problems in dynamic environments. The proposed algorithm modifies a recent version of evolutionary programming by introducing a simulated-annealing-like dynamic strategy parameter as well as applying local search towards the most improving directions. Diversity of the population is enhanced by an ensemble of external archives that serve as short-term and long-term memories. The archive members also act as the basic solutions when environmental changes occur. The algorithm is tested on a set of 6 multimodal problems with a total 49 change instances provided by CEC 2009 Competition on Evolutionary Computation in Dynamic and Uncertain Environments and the results are presented.