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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| Vydáno v: | Heliyon Ročník 11; číslo 1; s. e40087 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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England
Elsevier Ltd
15.01.2025
Elsevier |
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| ISSN: | 2405-8440, 2405-8440 |
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| Abstract | 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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| AbstractList | 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.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. 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. |
| ArticleNumber | e40087 |
| Author | Rashid, Tarik A. Ali, Yossra H. Rashed, Noor A. Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Noor A. surname: Rashed fullname: Rashed, Noor A. email: cs.20.62@grad.uotechnology.edu.iq organization: Computer Sciences Dept., Univ. of Technology, Baghdad, Iraq – sequence: 2 givenname: Yossra H. surname: Ali fullname: Ali, Yossra H. email: Yossra.h.ali@uotechnology.edu.iq organization: Computer Sciences Dept., Univ. of Technology, Baghdad, Iraq – sequence: 3 givenname: Tarik A. orcidid: 0000-0002-8661-258X surname: Rashid fullname: Rashid, Tarik A. email: tarik.ahmed@ukh.edu.krd organization: Computer Sciences & Engineering Dept., Artificial Intelligence Centre and Innovation, Univ. of Kurdistan Hewler, Iraq – sequence: 4 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali email: ali.Mirjalili@torrens.edu.au organization: Centre for Artificial Intelligence Research and Optimisation, Torrens University, Brisbane, QLD, 4006, QLD 4006, Austral, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39811291$$D View this record in MEDLINE/PubMed |
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| Keywords | Pareto optimality Real-world problems Multi-objective optimization Trade-off analysis decision-making challenges |
| Language | English |
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| SubjectTerms | algorithms data collection decision making Multi-objective optimization mutation Pareto optimality Real-world problems Trade-off analysis decision-making challenges |
| Title | MOANA: Multi-objective ant nesting algorithm for optimization problems |
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