企鹅抱团优化

M. M. al-Rifaie
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引用次数: 0

摘要

在我们的日常生活中,我们处理许多优化问题,其中一些微不足道,一些更复杂。这些问题经常使用多代理、基于人群的方法来解决。适用于复杂搜索空间和优化问题的技术的主要灵感来源之一是大自然。本文提出了一种新的元启发式方法——企鹅抱团优化(PHO),其灵感来自南极洲帝企鹅的抱团行为。该算法的简单性,即实现连续优化的一种范式,有助于分析其行为和推导更新方程中单个可调参数的最优值。一系列的实验试验证实了优化器在一组基准测试中的良好性能,以及与其他一些知名的基于种群的算法相比的竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Penguins Huddling Optimisation
In our everyday life, we deal with many optimisation problems, some of which trivial and some more complex. These problems have been frequently addressed using multiagent, population-based approaches. One of the main sources of inspiration for techniques applicable to complex search space and optimisation problems is nature. This paper proposes a new metaheuristic – Penguin Huddling Optimisation or PHO – whose inspiration is beckoned from the huddling behaviour of emperor penguins in Antarctica. The simplicity of the algorithm, which is the implementation of one such paradigm for continuous optimisation, facilitates the analysis of its behaviour and the derivation of the optimal value for its single adjustable parameter in the update equation. A series of experimental trials confirms the promising performance of the optimiser over a set of benchmarks, as well as its competitiveness when compared against few other well-known population based algorithms.
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