A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization
A coevolutionary technique based on multi-swarm particle swarm optimization for dynamic multi-objective optimization
复制标题
基于多群粒子群优化的动态多目标优化协同进化技术
DOI：
10.1016/j.ejor.2017.03.048
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发表时间：
2017-09-16
影响因子：
6.4
通讯作者：
Jiao, Licheng
中科院分区：
文献类型：
--
作者：
Liu, Ruochen;Li, Jianxia;Jiao, Licheng
在实际应用中，有许多领域涉及动态多目标优化问题（DMOP），其中目标相互冲突并随时间或环境而变化。本文提出了一种改进的协同进化多群粒子群优化算法（CMPSODMO）来求解快速变化环境中的DMOP问题。采用多群粒子群优化算法来解决动态环境下的优化问题。在CMPSODMO中，群体的数量由目标函数的数量决定，所有的群体利用信息共享策略进行协同进化。此外，一个新的速度更新方程和一个有效的边界约束技术的发展过程中的每个群体。然后，使用相似性检测算子来检测是否发生了变化，随后是基于存储器的动态机制来响应变化。建议CMPSODMO已被广泛比较，五个国家的最先进的算法在一套测试的基准问题。实验结果表明，该算法在处理快速变化环境中的DMOP问题上具有良好的应用前景。(C)2017爱思唯尔B.V.保留所有权利。
In real-world applications, there are many fields involving dynamic multi-objective optimization problems (DMOPs), in which objectives are in conflict with each other and change over time or environments. In this paper, a modified coevolutionary multi-swarm particle swarm optimizer is proposed to solve DMOPs in the rapidly changing environments (denoted as CMPSODMO). A frame of multi-swarm based particle swarm optimization is adopted to optimize the problem in dynamic environments. In CMPSODMO, the number of swarms (PSO) is determined by the number of the objective functions, and all of these swarms utilize an information sharing strategy to evolve cooperatively. Moreover, a new velocity update equation and an effective boundary constraint technique are developed during evolution of each swarm. Then, a similarity detection operator is used to detect whether a change has occurred, followed by a memory based dynamic mechanism to response to the change. The proposed CMPSODMO has been extensively compared with five state-of-the-art algorithms over a test suit of benchmark problems. Experimental results indicate that the proposed algorithm is promising for dealing with the DMOPs in the rapidly changing environments. (C) 2017 Elsevier B.V. All rights reserved.