星群优化中参数动态自适应的模糊逻辑方法

Emer Bernal, O. Castillo, J. Soria
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引用次数: 11

摘要

在本文中,我们提出了在星系群优化(GSO)方法中使用模糊系统动态调整参数。这个算法的灵感来自于恒星、星系和星系超星系团在重力作用下的运动。GSO使用探索和开发阶段的各种循环来实现探索新解决方案和利用现有解决方案之间的权衡。本文提出了c3和c4参数动态自适应的不同模糊系统,用17个不同维数的基准函数来衡量算法的性能。本文通过对不同变量的比较,证明了该方法在优化问题中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A fuzzy logic approach for dynamic adaptation of parameters in galactic swarm optimization
In this article we propose the use of fuzzy systems for dynamic adjustment of parameters in the galactic swarm optimization (GSO) method. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. GSO uses various cycles of exploration and exploitation phases to achieve a trade-off between the exploration of new solutions and exploitation of existing solutions. In this paper we proposed distinct fuzzy systems for the dynamic adaptation of the c3 and c4 parameters to measure the performance of the algorithm with 17 benchmark functions with different number of dimensions. In this paper a comparison was made between different variants to prove the efficacy of the method in optimization problems.
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