Investigation of the iCC framework performance for solving constrained LSGO problems : научное издание

Описание

Тип публикации: статья из журнала

Год издания: 2020

Идентификатор DOI: 10.3390/A13050108

Ключевые слова: Constrained optimization, Cooperative coevolution, Differential evolution, Evolution algorithms, Large-scale global optimization Evolutionary algorithms, Global optimization, Parameter estimation, Constrained real-parameter optimization, Cooperative co-evolution, Epsilon constrained methods, Large scale global optimizations, Optimization problems, Optimization techniques

Аннотация: Many modern real-valued optimization tasks use "black-box" (BB) models for evaluating objective functions and they are high-dimensional and constrained. Using common classifications, we can identify them as constrained large-scale global optimization (cLSGO) tasks. Today, the IEEE Congress of Evolutionary Computation provides a speПоказать полностьюcial session and several benchmarks for LSGO. At the same time, cLSGO problems are not well studied yet. The majority of modern optimization techniques demonstrate insufficient performance when confronted with cLSGO tasks. The effectiveness of evolution algorithms (EAs) in solving constrained low-dimensional optimization problems has been proven in many scientific papers and studies. Moreover, the cooperative coevolution (CC) framework has been successfully applied for EA used to solve LSGO problems. In this paper, a new approach for solving cLSGO has been proposed. This approach is based on CC and a method that increases the size of groups of variables at the decomposition stage (iCC) when solving cLSGO tasks. A new algorithm has been proposed, which combined the successhistory based parameter adaptation for differential evolution (SHADE) optimizer, iCC, and the ?-constrained method (namely ?-iCC-SHADE). We investigated the performance of the ?-iCC-SHADE and compared it with the previously proposed ?-CC-SHADE algorithm on scalable problems from the IEEE CEC 2017 Competition on constrained real-parameter optimization

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Издание

Журнал: Algorithms

Выпуск журнала: Vol. 13, Is. 5

Номера страниц: 108

Издатель: MDPI AG

Авторы

  • Vakhnin A. (31%Krasnoyarsk%660037%Russian Federation)
  • Sopov E. (79%Krasnoyarsk%660041%Russian Federation)

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