Enhanced Gaussian Bare-Bone Imperialist Competition Algorithm Based on Doubling Sampling and Quasi-oppositional Learning for Global Optimization

Enhanced Gaussian Bare-Bone Imperialist Competition Algorithm Based on Doubling Sampling and Quasi-oppositional Learning for Global Optimization

Abstract Gaussian bare-bone imperialist competitive algorithm (GBB-ICA) is an effective variant of imperialist competitive algorithm (ICA), which updates the position of colonies by sampling a Gaussian distribution. However, the mean and standard deviation adopted by GBB-ICA is calculated only using...

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Bibliographic Details
Main Authors: Dongge Lei, Lulu Cai, Fei Wu
Format: Article
Language:English
Published: Springer 2024-05-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Imperialist competition algorithm
Double Gaussian sampling
Oppositional learning
Global optimization
Online Access:https://doi.org/10.1007/s44196-024-00503-x

Internet

https://doi.org/10.1007/s44196-024-00503-x

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