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A multi-strategy enhanced northern goshawk optimization algorithm for global optimization and engineering design problems
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AI
DOI：10.1016/j.cma.2023.116199.png)
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Abstract
En 中文
Metaheuristic algorithms are widely utilized in various fields owing to their ability to produce a variety of solutions. The Northern Goshawk Optimization (NGO) is an effective optimization algorithm, however, its convergence rate is slow and it tends to fall into local optima in some cases. Therefore, this paper proposes a Multi-strategy Enhanced Northern Goshawk Optimization (MENGO) algorithm, which introduces a novel exploration strategy based on Levy flights to mitigate the risk of getting trapped in local optima. To balance exploration and exploitation, a new nonlinear reduction strategy based on the sine function is proposed. Additionally, a novel exploitation strategy is employed to accelerate the convergence speed while ensuring accuracy. The effectiveness of MENGO is demonstrated by comparing it with 13 advanced algorithms using 23 classical benchmark and 12 CEC2022 test functions in different dimensions. To evaluate the feasibility of the proposed approach in real-world applications, it is studied for nine constrained engineering problems, and the performance is compared with other contender algorithms extracted from the literature. The all experimental results show that MENGO outperforms other state-of-the-art algorithms in terms of solution quality and stability, making it a more competitive option. (c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Northern goshawk optimization
Metaheuristics
Swarm intelligence
Exploration strategy
Exploitation strategy
Journal
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7.3
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1.3W
Citations:
5.6W