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Ranking-Based Differential Evolution for Large-Scale Continuous Optimization

L. Guo - X. Li - W. Gong

Vydavateľ / Publisher:

Ústav informatiky SAV

Rok, strany / Year, pages: 2018, 49-75

DOI: https://doi.org/10.4149/cai_2018_1_49

Jazyk / Language: eng

Publikované / Published: 1. 6. 2018

Typ dokumentu / Type of Document:

Popis / Abstract:

Large-scale continuous optimization has gained considerable attention in recent years. Differential evolution (DE) is a simple yet efficient global numerical optimization algorithm, which has been successfully used in diverse fields. Generally, the vectors in the DE mutation operators are chosen randomly from the population. In this paper, we employ the ranking-based mutation operators for the DE algorithm to improve DE's performance. In the ranking-based mutation operators, the vectors are selected according to their rankings in the current population. The ranking-based mutation operators are general, and they are integrated into the original DE algorithm, GODE, and GaDE to verify the enhanced performance. Experiments have been conducted on the large-scale continuous optimization problems. The results indicate that the ranking-based mutation operators are able to enhance the overall performance of DE, GODE, and GaDE in the large-scale continuous optimization problems.

Ako citovať / How to Cite:

ISO 690:
Guo, L., Li, X., Gong, W. 2018. Ranking-Based Differential Evolution for Large-Scale Continuous Optimization. In Computing and Informatics, vol. 37, no.1, pp. 49-75. DOI: https://doi.org/10.4149/cai_2018_1_49

APA:
Guo, L., Li, X., Gong, W. (2018). Ranking-Based Differential Evolution for Large-Scale Continuous Optimization. Computing and Informatics, 37(1), 49-75. DOI: https://doi.org/10.4149/cai_2018_1_49

Kľúčové slová / Keywords:

Differential evolution, ranking-based mutation, vector selection, large-scale continuous optimization

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