区域差分元进化:一种求多变量函数所有理想最优的算法。

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Richard Wehr, Scott R Saleska
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引用次数: 0

摘要

区域差分元进化(TDME)是一种高效、通用、可靠的多变量函数全局或局部最优解求解算法。它采用渐进的小生境机制来优化具有多个全局最优和误导性局部最优的高维函数。本文介绍了TDME,并使用标准和新颖的基准问题来量化其相对于HillVallEA的优势,HillVallEA是自2013年以来所有主要多模态优化竞赛使用的标准基准套件上性能最好的算法。TDME在该基准测试套件上与HillVallEA相匹配,并在更全面的套件上明显优于HillVallEA,后者更好地反映了优化问题的潜在多样性。TDME无需任何特定于问题的参数调优即可实现该性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Territorial Differential Meta-Evolution: An Algorithm for Seeking All the Desirable Optima of a Multivariable Function.

Territorial Differential Meta-Evolution (TDME) is an efficient, versatile, and reliable algorithm for seeking all the global or desirable local optima of a multivariable function. It employs a progressive niching mechanism to optimize even challenging, high-dimensional functions with multiple global optima and misleading local optima. This paper introduces TDME and uses standard and novel benchmark problems to quantify its advantages over HillVallEA, which is the best-performing algorithm on the standard benchmark suite that has been used by all major multimodal optimization competitions since 2013. TDME matches HillVallEA on that benchmark suite and categorically outperforms it on a more comprehensive suite that better reflects the potential diversity of optimization problems. TDME achieves that performance without any problem-specific parameter tuning.

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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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