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Squid Game Optimizer (SGO): a novel metaheuristic algorithm.
Mahdi Azizi1,2, Milad Baghalzadeh Shishehgarkhaneh3, Mahla Basiri4,5
1Department of Civil Engineering, University of Tabriz, Tabriz, Iran. mehdi.azizi875@gmail.com.
Scientific Reports
|April 3, 2023
Summary
A new Squid Game Optimizer (SGO) uses game rules for optimization. This novel metaheuristic algorithm shows strong performance on benchmark and real-world problems.
Area of Science:
- Computational Intelligence
- Optimization Algorithms
- Metaheuristics
Background:
- Metaheuristic algorithms are crucial for solving complex optimization problems.
- Inspiration from real-world scenarios can lead to novel algorithmic approaches.
- Traditional games offer unique rule sets that can be mathematically modeled.
Purpose of the Study:
- To introduce the Squid Game Optimizer (SGO), a novel metaheuristic algorithm.
- To model the SGO based on the rules of the traditional Korean Squid Game.
- To evaluate the effectiveness and performance of the SGO algorithm.
Main Methods:
- Developed a mathematical model of the Squid Game Optimizer based on offensive and defensive player dynamics.
- Initialized a population of solution candidates randomly.
- Updated player positions based on objective function values and simulated game outcomes.
- Evaluated performance on 25 unconstrained 100-dimensional mathematical test functions.
- Compared SGO against six established metaheuristics using 100 independent optimization runs.
- Conducted statistical analyses including mean, standard deviation, and objective function evaluations.
- Utilized Kolmogorov-Smirnov, Mann-Whitney, and Kruskal-Wallis tests for comprehensive analysis.
- Assessed SGO on real-world problems from CEC 2020 benchmark suite.
Main Results:
- The Squid Game Optimizer (SGO) demonstrated competitive and remarkable outcomes across benchmark test functions.
- SGO showed outstanding performance in addressing complex real-world optimization problems from CEC 2020.
- Statistical analysis confirmed the significance and robustness of the SGO algorithm's performance.
- SGO outperformed or matched commonly used metaheuristics in various optimization tasks.
Conclusions:
- The proposed Squid Game Optimizer (SGO) is an effective and novel metaheuristic algorithm.
- SGO provides a unique approach to optimization inspired by game dynamics.
- The algorithm shows significant potential for application in both theoretical and practical optimization challenges.
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