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African vultures optimization algorithm based Choquet fuzzy integral for global optimization and engineering design problems
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DOI：10.1007/s10462-023-10602-4.png)
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Abstract
En 中文
Addressing complex optimization problems demands innovative solutions capable of navigating the interdependencies among variables, a reality often oversimplified by traditional metaheuristics. To address this challenge, this paper presents an enhanced African Vultures Optimization Algorithm, termed ci-AVOA, that incorporates the Choquet Integral, a powerful operator adept at considering criteria significance and interconnectedness in optimization scenarios. Unlike its predecessor, the ci-AVOA treats optimization problems in their true complexity by recognizing and accounting for the relationships between variables. The performance of ci-AVOA is evaluated on ten CEC2020 benchmark functions and four engineering design problems, pitted against other renowned optimization algorithms and the original AVOA. Across low and high dimensional benchmark functions, ci-AVOA consistently outperforms its counterparts, underpinning its superiority. This superior performance is further validated using non-parametric statistical tests, solidifying ci-AVOA as an effective and robust tool for tackling complex optimization problems. In essence, this study provides a significant contribution by augmenting a well-known metaheuristic with the Choquet Integral to devise a superior algorithm, ci-AVOA. This innovation extends the problem-solving capabilities of metaheuristics, promising more accurate and robust solutions for complex, real-world optimization problems.
Keywords:
Optimization
Metaheuristic
Choquet integral
Journal
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13.9
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6.1K
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