Seagull Optimization Algorithm (SOA)

A Novel Bio-inspired Optimization Algorithm
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Mise à jour 6 juin 2020

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The main inspiration of this algorithm is the migration and attacking behaviors of a seagull in nature. These behaviors are mathematically modeled and implemented to emphasize exploration and exploitation in a given search space. The performance of SOA algorithm
is compared with nine well-known metaheuristics on forty-four benchmark test functions. The analysis of computational complexity and convergence behaviors of the proposed algorithm have been evaluated. It is then employed to solve seven constrained real-life industrial applications to demonstrate its applicability. Experimental results reveal that the proposed algorithm is able to solve challenging large-scale constrained problems and is very competitive algorithm as compared with other optimization algorithms.

Cite it as: Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169-196.

Citation pour cette source

Gaurav Dhiman (2026). Seagull Optimization Algorithm (SOA) (https://fr.mathworks.com/matlabcentral/fileexchange/75180-seagull-optimization-algorithm-soa), MATLAB Central File Exchange. Extrait(e) le .

Compatibilité avec les versions de MATLAB
Créé avec R2020a
Compatible avec toutes les versions
Plateformes compatibles
Windows macOS Linux
Version Publié le Notes de version
2.0.0

This version increases the intensification and diversification capabilities of SOA algorithm.

1.0.0