MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems
Many-objective optimization (MaO) deals with a large number of conflicting objectives in optimization problems to acquire a reliable set of appropriate non-dominated solutions near the true Pareto front, and for the same, a unique mechanism is essential. Numerous papers have reported multi-...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Growing Science
2023-01-01
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| Series: | Decision Science Letters |
| Online Access: | http://www.growingscience.com/dsl/Vol12/dsl_2023_22.pdf |
| _version_ | 1797812589647888384 |
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| author | M. Premkumar Pradeep Jangir R. Sowmya Laith Abualigah |
| author_facet | M. Premkumar Pradeep Jangir R. Sowmya Laith Abualigah |
| author_sort | M. Premkumar |
| collection | DOAJ |
| description | Many-objective optimization (MaO) deals with a large number of conflicting objectives in optimization problems to acquire a reliable set of appropriate non-dominated solutions near the true Pareto front, and for the same, a unique mechanism is essential. Numerous papers have reported multi-objective evolutionary algorithms to explain the absence of convergence and diversity variety in many-objective optimization problems. One of the most encouraging methodologies utilizes many reference points to segregate the solutions and guide the search procedure. The above-said methodology is integrated into the basic version of the Moth Flame Optimization (MFO) algorithm for the first time in this paper. The proposed Many-Objective Moth Flame Optimization (MaOMFO) utilizes a set of reference points progressively decided by the hunt procedure of the moth flame. It permits the calculation to combine with the Pareto front yet synchronize the decent variety of the Pareto front. MaOMFO is employed to solve a wide range of unconstrained and constrained benchmark functions and compared with other competitive algorithms, such as non-dominated sorting genetic algorithm, multi-objective evolutionary algorithm based on dominance and decomposition, and novel multi-objective particle swarm optimization using different performance metrics. The results demonstrate the superiority of the algorithm as a new many-objective algorithm for complex many-objective optimization problems. |
| first_indexed | 2024-03-13T07:39:46Z |
| format | Article |
| id | doaj.art-8a0abea379ca45beb497897788fc01df |
| institution | Directory Open Access Journal |
| issn | 1929-5804 1929-5812 |
| language | English |
| last_indexed | 2024-03-13T07:39:46Z |
| publishDate | 2023-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | Decision Science Letters |
| spelling | doaj.art-8a0abea379ca45beb497897788fc01df2023-06-03T16:46:03ZengGrowing ScienceDecision Science Letters1929-58041929-58122023-01-0112357159010.5267/j.dsl.2023.4.006MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problemsM. PremkumarPradeep JangirR. SowmyaLaith Abualigah Many-objective optimization (MaO) deals with a large number of conflicting objectives in optimization problems to acquire a reliable set of appropriate non-dominated solutions near the true Pareto front, and for the same, a unique mechanism is essential. Numerous papers have reported multi-objective evolutionary algorithms to explain the absence of convergence and diversity variety in many-objective optimization problems. One of the most encouraging methodologies utilizes many reference points to segregate the solutions and guide the search procedure. The above-said methodology is integrated into the basic version of the Moth Flame Optimization (MFO) algorithm for the first time in this paper. The proposed Many-Objective Moth Flame Optimization (MaOMFO) utilizes a set of reference points progressively decided by the hunt procedure of the moth flame. It permits the calculation to combine with the Pareto front yet synchronize the decent variety of the Pareto front. MaOMFO is employed to solve a wide range of unconstrained and constrained benchmark functions and compared with other competitive algorithms, such as non-dominated sorting genetic algorithm, multi-objective evolutionary algorithm based on dominance and decomposition, and novel multi-objective particle swarm optimization using different performance metrics. The results demonstrate the superiority of the algorithm as a new many-objective algorithm for complex many-objective optimization problems.http://www.growingscience.com/dsl/Vol12/dsl_2023_22.pdf |
| spellingShingle | M. Premkumar Pradeep Jangir R. Sowmya Laith Abualigah MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems Decision Science Letters |
| title | MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems |
| title_full | MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems |
| title_fullStr | MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems |
| title_full_unstemmed | MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems |
| title_short | MaOMFO: Many-objective moth flame optimizer using reference-point based non-dominated sorting mechanism for global optimization problems |
| title_sort | maomfo many objective moth flame optimizer using reference point based non dominated sorting mechanism for global optimization problems |
| url | http://www.growingscience.com/dsl/Vol12/dsl_2023_22.pdf |
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