Snow ablation optimizer: A novel metaheuristic technique for numerical optimization and engineering design

This paper develops a novel nature-inspired metaheuristic technique named snow ablation optimizer (SAO) for numerical optimization and engineering design. The SAO algorithm mainly emulates the sublimation and melting behavior of snow to realize a tradeoff between exploitation and exploration in the...

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Vydáno v:Expert systems with applications Ročník 225; s. 120069
Hlavní autoři: Deng, Lingyun, Liu, Sanyang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.09.2023
Témata:
Benchmark
Engineering design
Novel metaheuristic algorithm
Premature convergence
Snow ablation optimizer
Engineering design
Benchmark
Snow ablation optimizer
Novel metaheuristic algorithm
Premature convergence
ISSN:0957-4174, 1873-6793
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Shrnutí:This paper develops a novel nature-inspired metaheuristic technique named snow ablation optimizer (SAO) for numerical optimization and engineering design. The SAO algorithm mainly emulates the sublimation and melting behavior of snow to realize a tradeoff between exploitation and exploration in the solution space and discourage premature convergence. The competitiveness and effectiveness of SAO are validated utilizing 29 typical CEC2017 unconstrained benchmarks and 22 CEC2020 real-world constrained optimization issues which consist of 7 process synthesis and design issues and 15 mechanical engineering issues. Additionally, to further verify its strength, the developed SAO is applied to extract the core parameters in photovoltaic systems. The simulation outcomes have demonstrated that the developed SAO is a very promising technique that can yield better performance than other state-of-the-art rival methods. The source code of SAO is publicly available at https://github.com/denglingyun123/SAO-snow-ablation-optimizer.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.120069