混沌差分进化的种群多样性研究

R. Šenkeřík, Adam Viktorin, Michal Pluhacek, T. Kadavy
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引用次数: 7

摘要

本研究涉及现代和流行的混沌动力学和进化计算的杂交。与许多将混沌与元启发式相结合的研究不同,本文侧重于更深入地了解种群动态,特别是混沌序列对种群多样性的影响。并记录了优化算法的性能。实验集中在一个简单的参数自适应差分进化(DE)策略:jDE算法的个体选择的不同随机化方案的广泛研究。jDE由9个不同的二维离散混沌系统驱动,作为混沌伪随机数生成器。在CEC 2015基准的二维设置(10D和30D)和15个测试函数上记录人口多样性和jDE收敛性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Population Diversity for the Chaotic Differential Evolution
This research deals with the modern and popular hybridization of chaotic dynamics and evolutionary computation. Unlike many research studies on the combination of chaos and metaheuristics, this paper focuses on the deeper insight into the population dynamics, specifically influence of chaotic sequences on the population diversity. The optimization algorithm performance was recorded as well. Experiments are focused on the extensive investigation of the different randomization schemes for the selection of individuals in a simple parameter adaptive Differential Evolution (DE) strategy: jDE algorithm. The jDE was driven by the nine different two-dimensional discrete chaotic systems, as the chaotic pseudo-random number generators. The population diversity and jDE convergence are recorded on the two-dimensional settings (10D and 30D) and 15 test functions from the CEC 2015 benchmark.
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