Cooperative co-evolution is recognized as an effective approach for solving large-scale optimization problems. It breaks down the problem dimensionality by splitting a large-scale problem into ones focusing on a smaller number of variables. This approach is successful when the studied problem is decomposable. However, many practical optimization problems can not be split into disjoint components. Most of them can be seen as interconnected components that share some variables with other ones. Such problems composed of parts that overlap each other are called overlapping problems. This paper proposes a modified cooperative co-evolutionary framework allowing to deal with non-disjoint subproblems in order to decompose and optimize overlapping problems efficiently. The proposed algorithm performs a new decomposition based on differential grouping to detect overlapping variables. A new cooperation strategy is also introduced to manage variables shared among several components. The performance of the new overlapped framework is assessed on large-scale overlapping benchmark problems derived from the CEC’2013 benchmark suite and compared with a state-of-the-art non-overlapped framework designed to tackle overlapping problems.
|titre||Optimization and Learning - 4th International Conference, OLA 2021, Proceedings|
|Sous-titre||4th International Conference, OLA 2021, Catania, Italy, June 21-23, 2021, Proceedings|
|rédacteurs en chef||Bernabé Dorronsoro, Patricia Ruiz, Lionel Amodeo, Mario Pavone|
|Nombre de pages||13|
|Etat de la publication||Publié - 17 août 2021|
Série de publications
|Nom||Communications in Computer and Information Science|
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Thèses de l'étudiant
Challenging High Dimensionality in Evolutionary Optimization using Cooperative Co-evolutionary AlgorithmsAuteur: Blanchard, J., 29 juin 2021
Student thesis: Doc types › Docteur en SciencesFichier