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Chaos Game Optimization (CGO)

version 1.0.0 (2.09 KB) by Siamak Talatahari
The Chaos Game Optimization (CGO) algorithm is a simple however efficient optimization meta-heuristic presented. The main concept of the CGO

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Updated 06 Dec 2020

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The Chaos Game Optimization (CGO) algorithm is a simple however efficient optimization meta-heuristic presented. The main concept of the CGO algorithm is based on some principles of chaos theory in which the configuration of fractals by chaos game methodology alongside the fractals self-similarity issues are in perspective.
Author and programmer: S. Talatahari, M. Azizi, Email: Siamak.Talat@gmail.com, mehdi.azizi875@gmail.com
Main paper:
1. S. Talatahari, M. Azizi, Chaos Game Optimization: a Novel Metaheuristic Algorithm, Artificial Intelligence Review, 2020, https://doi.org/10.1007/s10462-020-09867-w
2. S. Talatahari, M. Azizi, Optimization of Constrained Mathematical and Engineering Design Problems Using Chaos Game Optimization, Computers & Industrial Engineering, Volume 145, Pages 106560, https://doi.org/10.1016/j.cie.2020.106560

Cite As

Siamak Talatahari (2021). Chaos Game Optimization (CGO) (https://www.mathworks.com/matlabcentral/fileexchange/83938-chaos-game-optimization-cgo), MATLAB Central File Exchange. Retrieved .

Talatahari, Siamak, and Mahdi Azizi. “Chaos Game Optimization: a Novel Metaheuristic Algorithm.” Artificial Intelligence Review, Springer Science and Business Media LLC, June 2020, doi:10.1007/s10462-020-09867-w.

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Talatahari, Siamak, and Mahdi Azizi. “Optimization of Constrained Mathematical and Engineering Design Problems Using Chaos Game Optimization.” Computers & Industrial Engineering, vol. 145, Elsevier BV, July 2020, p. 106560, doi:10.1016/j.cie.2020.106560.

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MATLAB Release Compatibility
Created with R2020b
Compatible with any release
Platform Compatibility
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