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War Strategy Optimization Algorithm: A New Effective Metaheuristic Algorithm for Global Optimization
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DOI：10.1109/ACCESS.2022.3153493.png)
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This paper proposes a new metaheuristic optimization algorithm based on ancient war strategy. The proposed War Strategy Optimization (WSO) is based on the strategic movement of army troops during the war. War strategy is modeled as an optimization process wherein each soldier dynamically moves towards the optimum value. The proposed algorithm models two popular war strategies, attack and defense strategies. The positions of soldiers on the battlefield are updated in accordance with the strategy implemented. To improve the algorithm's convergence and robustness, a novel weight updating mechanism and a weak soldier's relocation strategy are introduced. The proposed war strategy algorithm achieves good balance of the exploration and exploitation stages. A detailed mathematical model of the algorithm is presented. The efficacy of the proposed algorithm is tested on 50 benchmark functions and four engineering problems. The performance of the algorithm is compared with ten popular metaheuristic algorithms. The experimental results for various optimization problems prove the superiority of the proposed algorithm.
Keyword:
Optimization
Classification algorithms
Metaheuristics
Heuristic algorithms
Search problems
Convergence
Particle swarm optimization
Metaheuristic
optimization
war strategy
swarm optimization
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
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