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Wild Geese Algorithm (WGA) for large scale optimization

version 1.0.3 (54.8 KB) by Ebrahim Akbari
A novel efficient algorithm for Large Scale Optimization, introduced in a 2021 paper

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Updated 19 Oct 2021

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In numerous real-life applications, nature-inspired population-based search algorithms have been applied to solve numerical optimization problems. The paper which is introduced at the end of this description focused on a simple and powerful swarm optimizer, named Wild Geese Algorithm (WGA), for large-scale global optimization whose efficiency and performance were verified using large-scale test functions of IEEE CEC 2008 and CEC 2010 special sessions with high dimensions D = 100, 500, 1000.
WGA was inspired by wild geese in nature and models various aspects of their life such as evolution, regular cooperative migration, and fatality. The effectiveness of WGA for finding the global optimal solutions of high dimensional optimization problems was compared with that of other methods reported in the previous literature. Experimental results showed that the proposed WGA has an efficient performance in solving a range of large-scale optimization problems, making it highly competitive among other large-scale optimization algorithms despite its simpler structure and easier implementation.
The reference paper (Open Access): https://doi.org/10.1016/j.array.2021.100074

Cite As

Ebrahim Akbari (2022). Wild Geese Algorithm (WGA) for large scale optimization (https://www.mathworks.com/matlabcentral/fileexchange/100848-wild-geese-algorithm-wga-for-large-scale-optimization), MATLAB Central File Exchange. Retrieved .

Ghasemi, Mojtaba, et al. Wild Geese Algorithm: A Novel Algorithm for Large Scale Optimization Based on the Natural Life and Death of Wild Geese. Elsevier BV, Sept. 2021, p. 100074, doi:10.1016/j.array.2021.100074.

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Created with R2021b
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