MOANA: Multi-objective ant nesting algorithm for optimization problems

This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporates adaptive mechanisms, such as deposition weight parameters, to balance explora...

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Veröffentlicht in:Heliyon Jg. 11; H. 1; S. e40087
Hauptverfasser: Rashed, Noor A., Ali, Yossra H., Rashid, Tarik A., Mirjalili, Seyedali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 15.01.2025
Elsevier
Schlagworte:
algorithms
data collection
decision making
Multi-objective optimization
mutation
Pareto optimality
Real-world problems
Trade-off analysis decision-making challenges
Pareto optimality
Real-world problems
Multi-objective optimization
Trade-off analysis decision-making challenges
ISSN:2405-8440, 2405-8440
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Zusammenfassung:This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporates adaptive mechanisms, such as deposition weight parameters, to balance exploration and exploitation, while a polynomial mutation strategy ensures diverse and high-quality solutions. The algorithm is evaluated on standard benchmark datasets, including ZDT functions and the IEEE Congress on Evolutionary Computation (CEC) 2019 multi-modal benchmarks. Comparative analysis against state-of-the-art algorithms like MOPSO, MOFDO, MODA, and NSGA-III demonstrates MOANA's superior performance in terms of convergence speed and Pareto front coverage. Furthermore, MOANA's applicability to real-world engineering optimization, such as welded beam design, showcases its ability to generate a broad range of optimal solutions, making it a practical tool for decision-makers. MOANA addresses key limitations of traditional evolutionary algorithms by improving scalability and diversity in multi-objective scenarios, positioning it as a robust solution for complex optimization tasks.
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ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e40087