A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms
Abstract In this research, a new method for population initialisation in meta‐heuristic algorithms based on the Pareto 80/20 rule is presented. The population in a meta‐heuristic algorithm has two important tasks, including pushing the algorithm toward the real optima and preventing the algorithm fr...
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| Format: | Article |
| Language: | English |
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Wiley
2021-10-01
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| Series: | IET Software |
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| Online Access: | https://doi.org/10.1049/sfw2.12025 |
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| author | Mohammad Reza Hasanzadeh Farshid Keynia |
| author_facet | Mohammad Reza Hasanzadeh Farshid Keynia |
| author_sort | Mohammad Reza Hasanzadeh |
| collection | DOAJ |
| description | Abstract In this research, a new method for population initialisation in meta‐heuristic algorithms based on the Pareto 80/20 rule is presented. The population in a meta‐heuristic algorithm has two important tasks, including pushing the algorithm toward the real optima and preventing the algorithm from trapping in the local optima. Therefore, the starting point of a meta‐heuristic algorithm can have a significant impact on the performance and output results of the algorithm. In this research, using the Pareto 80/20 rule, an innovative and new method for creating an initial population in meta‐heuristic algorithms is presented. In this method, by using elitism, it is possible to increase the convergence of the algorithm toward the global optima, and by using the complete distribution of the population in the search spaces, the algorithm is prevented from trapping in the local optima. In this research, the proposed initialisation method was implemented in comparison with other initialisation methods using the cuckoo search algorithm. In addition, the efficiency and effectiveness of the proposed method in comparison with other well‐known initialisation methods using statistical tests and in solving a variety of benchmark functions including unimodal, multimodal, fixed dimensional multimodal, and composite functions as well as in solving well‐known engineering problems was confirmed. |
| format | Article |
| id | doaj-art-a6af6e996a794a36a80c1cf9678c8463 |
| institution | Kabale University |
| issn | 1751-8806 1751-8814 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Software |
| spelling | doaj-art-a6af6e996a794a36a80c1cf9678c84632025-02-03T01:29:44ZengWileyIET Software1751-88061751-88142021-10-0115532334710.1049/sfw2.12025A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithmsMohammad Reza Hasanzadeh0Farshid Keynia1Department of Computer and Information Technology Islamic Azad University Kerman IranDepartment of Energy Management and Optimization Institute of Science and High Technology and Environmental Sciences Graduate University of Advanced Technology Kerman IranAbstract In this research, a new method for population initialisation in meta‐heuristic algorithms based on the Pareto 80/20 rule is presented. The population in a meta‐heuristic algorithm has two important tasks, including pushing the algorithm toward the real optima and preventing the algorithm from trapping in the local optima. Therefore, the starting point of a meta‐heuristic algorithm can have a significant impact on the performance and output results of the algorithm. In this research, using the Pareto 80/20 rule, an innovative and new method for creating an initial population in meta‐heuristic algorithms is presented. In this method, by using elitism, it is possible to increase the convergence of the algorithm toward the global optima, and by using the complete distribution of the population in the search spaces, the algorithm is prevented from trapping in the local optima. In this research, the proposed initialisation method was implemented in comparison with other initialisation methods using the cuckoo search algorithm. In addition, the efficiency and effectiveness of the proposed method in comparison with other well‐known initialisation methods using statistical tests and in solving a variety of benchmark functions including unimodal, multimodal, fixed dimensional multimodal, and composite functions as well as in solving well‐known engineering problems was confirmed.https://doi.org/10.1049/sfw2.12025Pareto optimisationsearch problemsstatistical testingmetaheuristics |
| spellingShingle | Mohammad Reza Hasanzadeh Farshid Keynia A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms IET Software Pareto optimisation search problems statistical testing metaheuristics |
| title | A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms |
| title_full | A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms |
| title_fullStr | A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms |
| title_full_unstemmed | A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms |
| title_short | A new population initialisation method based on the Pareto 80/20 rule for meta‐heuristic optimisation algorithms |
| title_sort | new population initialisation method based on the pareto 80 20 rule for meta heuristic optimisation algorithms |
| topic | Pareto optimisation search problems statistical testing metaheuristics |
| url | https://doi.org/10.1049/sfw2.12025 |
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