MOCBOA: Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving Engineering Design Problems

Multi-objective optimization is critical for problem-solving in engineering, economics, and AI. This study introduces the Multi-Objective Chef-Based Optimization Algorithm (MOCBOA), an upgraded version of the Chef-Based Optimization Algorithm (CBOA) that addresses distinct objectives. Our approach i...

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Vydáno v:Computer modeling in engineering & sciences Ročník 143; číslo 1; s. 967 - 1008
Hlavní autoři: Chalabi, Nour Elhouda, Attia, Abdelouahab, Almazyad, Abdulaziz S., Mohamed, Ali Wagdy, Werner, Frank, Jangir, Pradeep, Shokouhifar, Mohammad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Henderson Tech Science Press 2025
Témata:
Algorithms
Archives & records
Chemical engineering
Computer engineering
Design engineering
Design optimization
Genetic algorithms
Knowledge sharing
Multiple objective analysis
Objectives
Optimization algorithms
Pareto optimization
Pareto optimum
Problem solving
R&D
Research & development
Science
ISSN:1526-1506, 1526-1492, 1526-1506
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Shrnutí:Multi-objective optimization is critical for problem-solving in engineering, economics, and AI. This study introduces the Multi-Objective Chef-Based Optimization Algorithm (MOCBOA), an upgraded version of the Chef-Based Optimization Algorithm (CBOA) that addresses distinct objectives. Our approach is unique in systematically examining four dominance relations—Pareto, Epsilon, Cone-epsilon, and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front. Our comparison investigation, which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering, mechanical design, and power systems, reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric. This paper provides a solid foundation for determining the most effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.
Bibliografie:ObjectType-Article-1
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ISSN:1526-1506
1526-1492
1526-1506
DOI:10.32604/cmes.2025.062332