Multiguiders and Nondominate Ranking Differential Evolution Algorithm for Multiobjective Global Optimization of Electromagnetic Problems
The differential evolution (DE) algorithm was initially developed for single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multiobjective global optimization (MOGO) recently. In general multiobjective d...
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| Vydáno v: | IEEE transactions on magnetics Ročník 49; číslo 5; s. 2105 - 2108 |
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| Médium: | Journal Article Konferenční příspěvek |
| Jazyk: | angličtina |
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New York, NY
IEEE
01.05.2013
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9464, 1941-0069 |
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| Abstract | The differential evolution (DE) algorithm was initially developed for single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multiobjective global optimization (MOGO) recently. In general multiobjective differential evolutionary algorithm, only use conventional DE strategies, and, in order to optimize performance constrains problems, the feasibility of the solutions was considered only at selection step. This paper presents a new multiobjective evolutionary algorithm based on differential evolution. In the mutation step, the proposed method which applied multiguiders instead of conventional base vector selection method is used. Therefore, feasibility of multiguiders, involving constraint optimization problems, is also considered. Furthermore, the approach also incorporates nondominated sorting method and secondary population for the nondominated solutions. The propose algorithm is compared with resent approaches of multiobjective optimizers in solving multiobjective version of Testing Electromagnetic Analysis Methods (TEAM) problem 22. |
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| AbstractList | The differential evolution (DE) algorithm was initially developed for single-objective problems and was shown to be a fast, simple algorithm. In order to utilize these advantages in real-world problems it was adapted for multiobjective global optimization (MOGO) recently. In general multiobjective differential evolutionary algorithm, only use conventional DE strategies, and, in order to optimize performance constrains problems, the feasibility of the solutions was considered only at selection step. This paper presents a new multiobjective evolutionary algorithm based on differential evolution. In the mutation step, the proposed method which applied multiguiders instead of conventional base vector selection method is used. Therefore, feasibility of multiguiders, involving constraint optimization problems, is also considered. Furthermore, the approach also incorporates nondominated sorting method and secondary population for the nondominated solutions. The propose algorithm is compared with resent approaches of multiobjective optimizers in solving multiobjective version of Testing Electromagnetic Analysis Methods (TEAM) problem 22. |
| Author | Baatar, Nyambayar Koh, Chang-Seop Pham, Minh-Trien |
| Author_xml | – sequence: 1 givenname: Nyambayar surname: Baatar fullname: Baatar, Nyambayar organization: <formula formulatype="inline"><tex Notation="TeX">$^{1}$</tex> </formula>College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Chungbuk, Korea – sequence: 2 givenname: Minh-Trien surname: Pham fullname: Pham, Minh-Trien organization: <formula formulatype="inline"><tex Notation="TeX">$^{2}$</tex> </formula>University of Engineering and Technology, Vietnam National University, Hanoi 100000, Vietnam – sequence: 3 givenname: Chang-Seop surname: Koh fullname: Koh, Chang-Seop email: kohcs@cbnu.ac.kr organization: <formula formulatype="inline"><tex Notation="TeX">$^{1}$</tex></formula>College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Chungbuk, Korea |
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| Keywords | multiobjective optimization Electromagnetism Analysis method Testing Electromagnetic Analysis Methods (TEAM) problem 22 Investigation method multiguiders Test method Differential evolution nondominated ranking Magnetic properties |
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| SubjectTerms | Algorithm design and analysis Algorithms Cross-disciplinary physics: materials science; rheology Differential evolution Evolution Evolutionary algorithms Exact sciences and technology Feasibility Indexes Magnetism Materials science Mathematical analysis multiguiders multiobjective optimization nondominated ranking Optimization Other topics in materials science Physics Sociology Statistics Strategy Studies Superconducting magnetic energy storage Testing Electromagnetic Analysis Methods (TEAM) problem 22 Vectors Vectors (mathematics) |
| Title | Multiguiders and Nondominate Ranking Differential Evolution Algorithm for Multiobjective Global Optimization of Electromagnetic Problems |
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