Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic Algorithm
A large number of real-world issues are among difficult and multi-objective problems. Recently, it has been recognised that the evolutionary algorithms optimise well these types of problems. This paper proposes a novel multi-objective search algorithm that is called the Spacing Multi-Objective Genet...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-07-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2018.1443319 |
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| author | L. Falahiazar H. Shah-Hosseini |
| author_facet | L. Falahiazar H. Shah-Hosseini |
| author_sort | L. Falahiazar |
| collection | DOAJ |
| description | A large number of real-world issues are among difficult and multi-objective problems. Recently, it has been recognised that the evolutionary algorithms optimise well these types of problems. This paper proposes a novel multi-objective search algorithm that is called the Spacing Multi-Objective Genetic Algorithm (Spacing-MOGA). The innovation of the proposed Spacing-MOGA lies in a new survival selection algorithm called Spacing Distance. This research eliminates some of the disadvantages of other algorithms such as the Non-dominated Sorting Genetic Algorithm II (NSGAII). The proposed Spacing-MOGA is applied to five test benchmark functions and also to the design of I-Beam. Then, the results are compared with other algorithms such as NSGAII, Adaptive Weighted Particle Swarm Optimisation (AWPSO), and Non-dominated Sorting Particle Swarm Optimiser (NSPSO) based on the test metrics: Hypervolume, Spacing, Spread, and Generational Distance. Furthermore, for further demonstration of the ability of the proposed Spacing-MOGA, the experimental results are evaluated by the t-test. |
| format | Article |
| id | doaj-art-963e83c3aaca4f3abb7abfee6cdbc297 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2018-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-963e83c3aaca4f3abb7abfee6cdbc2972025-08-20T01:58:19ZengTaylor & Francis GroupConnection Science0954-00911360-04942018-07-0130332634210.1080/09540091.2018.14433191443319Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic AlgorithmL. Falahiazar0H. Shah-Hosseini1Islamic Azad UniversityIslamic Azad UniversityA large number of real-world issues are among difficult and multi-objective problems. Recently, it has been recognised that the evolutionary algorithms optimise well these types of problems. This paper proposes a novel multi-objective search algorithm that is called the Spacing Multi-Objective Genetic Algorithm (Spacing-MOGA). The innovation of the proposed Spacing-MOGA lies in a new survival selection algorithm called Spacing Distance. This research eliminates some of the disadvantages of other algorithms such as the Non-dominated Sorting Genetic Algorithm II (NSGAII). The proposed Spacing-MOGA is applied to five test benchmark functions and also to the design of I-Beam. Then, the results are compared with other algorithms such as NSGAII, Adaptive Weighted Particle Swarm Optimisation (AWPSO), and Non-dominated Sorting Particle Swarm Optimiser (NSPSO) based on the test metrics: Hypervolume, Spacing, Spread, and Generational Distance. Furthermore, for further demonstration of the ability of the proposed Spacing-MOGA, the experimental results are evaluated by the t-test.http://dx.doi.org/10.1080/09540091.2018.1443319multi-objective problemsnon-dominated sorting genetic algorithm iinon-dominated sorting particle swarm optimiseradaptive weighted particle swarm optimisationi-beam |
| spellingShingle | L. Falahiazar H. Shah-Hosseini Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic Algorithm Connection Science multi-objective problems non-dominated sorting genetic algorithm ii non-dominated sorting particle swarm optimiser adaptive weighted particle swarm optimisation i-beam |
| title | Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic Algorithm |
| title_full | Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic Algorithm |
| title_fullStr | Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic Algorithm |
| title_full_unstemmed | Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic Algorithm |
| title_short | Optimisation of engineering system using a novel search algorithm: the Spacing Multi-Objective Genetic Algorithm |
| title_sort | optimisation of engineering system using a novel search algorithm the spacing multi objective genetic algorithm |
| topic | multi-objective problems non-dominated sorting genetic algorithm ii non-dominated sorting particle swarm optimiser adaptive weighted particle swarm optimisation i-beam |
| url | http://dx.doi.org/10.1080/09540091.2018.1443319 |
| work_keys_str_mv | AT lfalahiazar optimisationofengineeringsystemusinganovelsearchalgorithmthespacingmultiobjectivegeneticalgorithm AT hshahhosseini optimisationofengineeringsystemusinganovelsearchalgorithmthespacingmultiobjectivegeneticalgorithm |