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Deep memetic models for combinatorial optimization problems: application to the tool switching problem
Memetic Computing ( IF 4.7 ) Pub Date : 2019-09-12 , DOI: 10.1007/s12293-019-00294-1
Jhon Edgar Amaya , Carlos Cotta , Antonio J. Fernández-Leiva , Pablo García-Sánchez

Memetic algorithms are techniques that orchestrate the interplay between population-based and trajectory-based algorithmic components. In particular, some memetic models can be regarded under this broad interpretation as a group of autonomous basic optimization algorithms that interact among them in a cooperative way in order to deal with a specific optimization problem, aiming to obtain better results than the algorithms that constitute it separately. Going one step beyond this traditional view of cooperative optimization algorithms, this work tackles deep meta-cooperation, namely the use of cooperative optimization algorithms in which some components can in turn be cooperative methods themselves, thus exhibiting a deep algorithmic architecture. The objective of this paper is to demonstrate that such models can be considered as an efficient alternative to other traditional forms of cooperative algorithms. To validate this claim, different structural parameters, such as the communication topology between the agents, or the parameter that influences the depth of the cooperative effort (the depth of meta-cooperation), have been analyzed. To do this, a comparison with the state-of-the-art cooperative methods to solve a specific combinatorial problem, the Tool Switching Problem, has been performed. Results show that deep models are effective to solve this problem, outperforming metaheuristics proposed in the literature.

中文翻译:

组合优化问题的深模因模型:在工具切换问题中的应用

模因算法是协调基于总体和基于轨迹的算法组件之间相互作用的技术。特别是,在这种广泛的解释下,一些模因模型可以看作是一组自主的基本优化算法,它们以协作的方式在它们之间相互作用,以处理特定的优化问题,目的是获得比构成其的算法更好的结果。分别。这项工作超越了传统的协作优化算法的观点,解决了深层次的元协作,即使用协作优化算法,其中某些组件本身又可以成为协作方法,从而展现出一种深层的算法体系结构。本文的目的是证明可以将此类模型视为其他传统形式的协作算法的有效替代方案。为了验证此主张,已分析了不同的结构参数,例如代理之间的通信拓扑,或影响合作努力深度(元合作深度)的参数。为此,已与解决特定组合问题(工具切换问题)的最新合作方法进行了比较。结果表明,较之文献中提出的元启发式方法,深度模型可有效解决此问题。或影响合作努力深度(元合作深度)的参数已被分析。为此,已与解决特定组合问题(工具切换问题)的最新合作方法进行了比较。结果表明,较之文献中提出的元启发式方法,深度模型可有效解决此问题。或影响合作努力深度(元合作深度)的参数已被分析。为此,已与解决特定组合问题(工具切换问题)的最新合作方法进行了比较。结果表明,较之文献中提出的元启发式方法,深度模型可有效解决此问题。
更新日期:2019-09-12
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