Tracking optima in dynamic problems by an optimizer based on piecewise-rotational chaos system
Tracking optima in dynamic problems by an optimizer based on piecewise-rotational chaos system
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DOI：
10.1587/nolta.9.497
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Yoshikazu Yamanaka;T. Tsubone
中科院分区：
文献类型：
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作者：
Yoshikazu Yamanaka;T. Tsubone
提出了一种基于分段旋转混沌系统(OPRC)的多种群优化算法，在容差范围内进行记忆更新，实现了动态问题的最优跟踪。对于基于多种群的优化器来说，跟踪最优是一项diffi崇拜任务，因为有两个问题，称为过时记忆和发散损失。为了解决内存过时的问题，提出了一种简单的容差内内存更新过程。将该方法应用于我们之前提出的OPRC优化器中，观察到了它出色的跟踪性能。这一结果表明，该算法在不改变搜索动力学的情况下，能够解决发散损失问题。fi。文中还考虑了OPRC的跟踪机制，发现混沌系统的折叠动力学给出的搜索行为有助于捕获移位最优解。
: Tracking optima in dynamic problems is achieved by a multi-population optimizer based on piecewise-rotational chaotic system (OPRC) using memory update within a tolerance. Tracking optima is a difficult task for multi-population-based optimizers because of two issues, called outdated memory and divergent loss. To solve the outdated memory issue, a simple procedure named memory update within a tolerance is proposed. The proposed procedure is applied to our previous proposed optimizer OPRC, and its outstanding tracking performance is observed. This result shows OPRC can solve the divergent loss issue without any modification of its searching dynamics. The tracking mechanism of OPRC is also considered, and it is uncovered that the searching behavior given by the folding dynamics of the chaotic system contributes to catch the shifting optima.