Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9 September 2021, Sakarya, Turkey

Research Article

Solving Suggested Problems using Grey Wolf Optimization

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  • @INPROCEEDINGS{10.4108/eai.7-9-2021.2314893,
        author={Nawras A. Issa and Zaid  Alaa and Issa Ahmed Abed},
        title={Solving Suggested Problems using Grey Wolf Optimization},
        proceedings={Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9 September 2021, Sakarya, Turkey},
        publisher={EAI},
        proceedings_a={IMDC-IST},
        year={2022},
        month={1},
        keywords={metaheuristic algorithm grey wolfe optimizer},
        doi={10.4108/eai.7-9-2021.2314893}
    }
    
  • Nawras A. Issa
    Zaid Alaa
    Issa Ahmed Abed
    Year: 2022
    Solving Suggested Problems using Grey Wolf Optimization
    IMDC-IST
    EAI
    DOI: 10.4108/eai.7-9-2021.2314893
Nawras A. Issa1,*, Zaid Alaa2, Issa Ahmed Abed2
  • 1: Basrah Engineering Technical College, Southern Technical University, Iraq
  • 2: Management Technical College – Basra, Southern Technical University, Iraq
*Contact email: nwrsb372@gmail.com

Abstract

In this research, the Grey Wolf Optimizer (GWO) algorithm was applied, which is a type of metaheuristic optimization algorithms that is important for identifying problems and finding ways to improve them. In 2014, Seyedali Mirjalili came up with this algorithm. Its social hierarchy is modeled after that of grey wolves in nature. These wolves live in groups consisting of( 5 – 12) individuals. Wolves are divided into four levels, alpha represents the first level it is accountable for the manufacture of important resolutions for the peak like hunting, bedtime, wake up, etc. As for the second level in the hierarchy, it represents the beta wolf and is the advisor of the alpha. Beta can be the leader after the death of one of the alpha wolves. The third level represents delta, which follows the commands of alpha and beta. it is the dominant omega. omega represents the last level, which obeys all other wolves. Furthermore, the chief algorithm stages such as chasing, searching the prey, encircle and attack the prey were applied. GWO algorithm was tested on three benchmarks test functions using MATLAB R2014a. the results were confirmed by comparing GWO through another intelligent swarm algorithm like Particle Swarm Optimization (PSO) algorithm. results showed the superiority GWO in achieving better results and high convergence speed.