Using the iCC framework for solving unconstrained LSGO problems : доклад, тезисы доклада

Vakhnin A.A.; Sopov E.E.

База данных: Каталог библиотеки СФУ

Аннотация: Evolutionary computation is a branch of computational intelligence that is inspired by nature. This algorithm family uses fundamental rules of natural selection to find a better solution when solving optimization problems. The productivity of these methods decreases when the number of parameters to be optimized is extra-large. This area of research is known as large-scale global optimization. Cooperative coevolution is a structure for evolutionary algorithms which tries to solve the curse of dimensionality problem. The cooperative coevolution efficiency of application largely depends on the two settings: the size of each subcomponent(subproblem) and the grouping of variables. The actual work proposes improved cooperative coevolution denoted as iCC. The iCC approach dynamically resizes the groups. iCC starts with a predefined set of subproblems and reduces them gradually during the optimization process. A novel metaheuristic has been developed which is called iCC-SHADE for black-box optimization problems with a large number of variables. The proposed method has been tested on fifteen optimization tasks from the LSGO CEC'2013 competition benchmark. The experimental results have demonstrated that iCC-SHADE has statistically better performance than CC-SHADE with a static number of subproblems. Also, the effectiveness of iCC-SHADE has been tested in comparison with other modern metaheuristics. The Wilcoxon rank-sum test was used to compare the effectiveness of investigated metaheuristics.

Год издания: 2021

Авторы: Vakhnin A.A., Sopov E.E.

Источник: IOP Conference Series: Materials Science and Engineering

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Using the iCC framework for solving unconstrained LSGO problems : доклад, тезисы доклада
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