Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables
Recently, it was demonstrated that multitasking evolutionary algorithm (MTEA), a newly proposed algorithm, can solve multiple optimization problems simultaneously through a single run, breaking through the limitations of traditional evolutionary algorithms (EAs), with good convergence and exploratio...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/4609489 |
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| _version_ | 1832547048296022016 |
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| author | Lei Wang Qian Sun Qingzheng Xu Wei Li Qiaoyong Jiang |
| author_facet | Lei Wang Qian Sun Qingzheng Xu Wei Li Qiaoyong Jiang |
| author_sort | Lei Wang |
| collection | DOAJ |
| description | Recently, it was demonstrated that multitasking evolutionary algorithm (MTEA), a newly proposed algorithm, can solve multiple optimization problems simultaneously through a single run, breaking through the limitations of traditional evolutionary algorithms (EAs), with good convergence and exploration performance. As a novel algorithm, MTEA still has a lot of unexplored space. Generally speaking, the order of solution variables has no significant influence on the single-tasking EAs. To our knowledge, the effect of the order of variables in the multitasking scenario has not been explored. To fill in this research gap, three orders of variables in the multitasking scenario are proposed in this paper, including full reverse order, bisection reverse order, and trisection reverse order. An important feature of these orders of variables is that an individual can recover as himself after two times of changing the order of variables. In order to verify our idea, these orders of variables are embedded into MTEA. The experiment results revealed that the effect of the different orders of variables is universal but not significant enough in the practical application. Furthermore, tasks with high similarity and high degree of intersection are sensitive to the order of variables and get great impact between tasks. |
| format | Article |
| id | doaj-art-e741663f3ed8481cbd0239f5e57f4f40 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e741663f3ed8481cbd0239f5e57f4f402025-02-03T06:46:21ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/46094894609489Analysis of Multitasking Evolutionary Algorithms under the Order of Solution VariablesLei Wang0Qian Sun1Qingzheng Xu2Wei Li3Qiaoyong Jiang4School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaCollege of Information and Communication, National University of Defense Technology, Xi’an 710106, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaRecently, it was demonstrated that multitasking evolutionary algorithm (MTEA), a newly proposed algorithm, can solve multiple optimization problems simultaneously through a single run, breaking through the limitations of traditional evolutionary algorithms (EAs), with good convergence and exploration performance. As a novel algorithm, MTEA still has a lot of unexplored space. Generally speaking, the order of solution variables has no significant influence on the single-tasking EAs. To our knowledge, the effect of the order of variables in the multitasking scenario has not been explored. To fill in this research gap, three orders of variables in the multitasking scenario are proposed in this paper, including full reverse order, bisection reverse order, and trisection reverse order. An important feature of these orders of variables is that an individual can recover as himself after two times of changing the order of variables. In order to verify our idea, these orders of variables are embedded into MTEA. The experiment results revealed that the effect of the different orders of variables is universal but not significant enough in the practical application. Furthermore, tasks with high similarity and high degree of intersection are sensitive to the order of variables and get great impact between tasks.http://dx.doi.org/10.1155/2020/4609489 |
| spellingShingle | Lei Wang Qian Sun Qingzheng Xu Wei Li Qiaoyong Jiang Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables Complexity |
| title | Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables |
| title_full | Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables |
| title_fullStr | Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables |
| title_full_unstemmed | Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables |
| title_short | Analysis of Multitasking Evolutionary Algorithms under the Order of Solution Variables |
| title_sort | analysis of multitasking evolutionary algorithms under the order of solution variables |
| url | http://dx.doi.org/10.1155/2020/4609489 |
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