An Adaptive Framework to Tune the Coordinate Systems in Evolutionary Algorithms
Zhi-Zhong Liu, Yong Wang, Shengxiang Yang, and Ke Tang

TL;DR
This paper introduces ACoS, an adaptive framework that dynamically tunes coordinate systems in evolutionary algorithms, improving search efficiency by leveraging population distribution information and combining multiple coordinate systems.
Contribution
The paper presents a novel adaptive framework, ACoS, that enhances EAs by dynamically selecting coordinate systems based on landscape features, applicable to PSO and DE.
Findings
ACoS improves optimization performance on benchmark functions.
Adaptive coordinate tuning outperforms fixed systems.
Effective in high-dimensional problems.
Abstract
In the evolutionary computation research community, the performance of most evolutionary algorithms (EAs) depends strongly on their implemented coordinate system. However, the commonly used coordinate system is fixed and not well suited for different function landscapes, EAs thus might not search efficiently. To overcome this shortcoming, in this paper we propose a framework, named ACoS, to adaptively tune the coordinate systems in EAs. In ACoS, an Eigen coordinate system is established by making use of the cumulative population distribution information, which can be obtained based on a covariance matrix adaptation strategy and an additional archiving mechanism. Since the population distribution information can reflect the features of the function landscape to some extent, EAs in the Eigen coordinate system have the capability to identify the modality of the function landscape. In…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Advanced Multi-Objective Optimization Algorithms
