A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems
In this paper, mixed-integer hybrid differential evolution (MIHDE) is developed to deal with the mixed-integer optimization problems. This hybrid algorithm contains the migration operation to avoid candidate individuals clustering together. We introduce the population diversity measure to inspect wh...
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| Vydáno v: | Computers & mathematics with applications (1987) Ročník 47; číslo 8; s. 1295 - 1307 |
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| Jazyk: | angličtina |
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Elsevier Ltd
01.04.2004
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| ISSN: | 0898-1221, 1873-7668 |
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| Abstract | In this paper, mixed-integer hybrid differential evolution (MIHDE) is developed to deal with the mixed-integer optimization problems. This hybrid algorithm contains the migration operation to avoid candidate individuals clustering together. We introduce the population diversity measure to inspect when the migration operation should be performed so that the user can use a smaller population size to obtain a global solution. A mixed coding representation and a rounding operation are introduced in MIHDE so that the hybrid algorithm is not only used to solve the mixed-integer nonlinear optimization problems, but also used to solve the real and integer nonlinear optimization problems. Some numerical examples are tested to illustrate the performance of the proposed algorithm. Numerical examples show that the proposed algorithm converges to better solutions than the conventional genetic algorithms. |
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| AbstractList | In this paper, mixed-integer hybrid differential evolution (MIHDE) is developed to deal with the mixed-integer optimization problems. This hybrid algorithm contains the migration operation to avoid candidate individuals clustering together. We introduce the population diversity measure to inspect when the migration operation should be performed so that the user can use a smaller population size to obtain a global solution. A mixed coding representation and a rounding operation are introduced in MIHDE so that the hybrid algorithm is not only used to solve the mixed-integer nonlinear optimization problems, but also used to solve the real and integer nonlinear optimization problems. Some numerical examples are tested to illustrate the performance of the proposed algorithm. Numerical examples show that the proposed algorithm converges to better solutions than the conventional genetic algorithms. |
| Author | Wang, Feng-Sheng Hwang, Kao-Shing Lin, Yung-Chien |
| Author_xml | – sequence: 1 givenname: Yung-Chien surname: Lin fullname: Lin, Yung-Chien organization: Department of Electrical Engineering, National Chung Cheng University Chia-Yi 621-02, Taiwan, R.O.C – sequence: 2 givenname: Kao-Shing surname: Hwang fullname: Hwang, Kao-Shing organization: Department of Electrical Engineering, National Chung Cheng University Chia-Yi 621-02, Taiwan, R.O.C – sequence: 3 givenname: Feng-Sheng surname: Wang fullname: Wang, Feng-Sheng email: chmfsw@ccunix.ccu.edu.tw organization: Department of Chemical Engineering, National Chung Cheng University Chia-Yi 621-02, Taiwan, Taiwan, R.O.C |
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| Cites_doi | 10.1109/59.486107 10.1016/S0898-1221(97)00229-0 10.1021/ie9702486 10.1016/S0098-1354(00)00653-0 10.1023/A:1008202821328 10.1080/03052159108941075 10.1016/S0377-2217(96)00197-X 10.1090/dimacs/040/25 10.1016/0098-1354(90)87009-E 10.1109/43.372366 10.1021/ie970736d 10.1115/1.2912596 10.1109/49.510907 10.1016/S0009-2509(00)00038-5 10.1016/0360-8352(96)00041-1 |
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| Keywords | Differential evolution Hybrid method Evolutionary algorithms Mixed-integer nonlinear programming Genetic algorithms |
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