A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives
Traditionally, evolutionary algorithms (EAs) have been systematically developed to solve mono-, multi-, and many-objective optimization problems, in this order. Despite some efforts in unifying different types of mono-objective evolutionary and non-EAs, researchers are not interested enough in unify...
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| Vydané v: | IEEE transactions on evolutionary computation Ročník 20; číslo 3; s. 358 - 369 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.06.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1089-778X, 1941-0026 |
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| Abstract | Traditionally, evolutionary algorithms (EAs) have been systematically developed to solve mono-, multi-, and many-objective optimization problems, in this order. Despite some efforts in unifying different types of mono-objective evolutionary and non-EAs, researchers are not interested enough in unifying all three types of optimization problems together. Such a unified algorithm will allow users to work with a single software enabling one-time implementation of solution representation, operators, objectives, and constraints formulations across several objective dimensions. For the first time, we propose a unified evolutionary optimization algorithm for solving all three classes of problems specified above, based on the recently proposed elitist, guided nondominated sorting procedure, developed for solving many-objectives problems. Using a new niching-based selection procedure, our proposed unified algorithm automatically degenerates to an efficient equivalent population-based algorithm for each class. No extra parameters are needed. Extensive simulations are performed on unconstrained and constrained test problems having single-, two-, multi-, and many-objectives and on two engineering optimization design problems. Performance of the unified approach is compared to suitable population-based counterparts at each dimensional level. Results amply demonstrate the merit of our proposed unified approach and motivate similar studies for a richer understanding of the development of optimization algorithms. |
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| AbstractList | Traditionally, evolutionary algorithms (EAs) have been systematically developed to solve mono-, multi-, and many-objective optimization problems, in this order. Despite some efforts in unifying different types of mono-objective evolutionary and non-EAs, researchers are not interested enough in unifying all three types of optimization problems together. Such a unified algorithm will allow users to work with a single software enabling one-time implementation of solution representation, operators, objectives, and constraints formulations across several objective dimensions. For the first time, we propose a unified evolutionary optimization algorithm for solving all three classes of problems specified above, based on the recently proposed elitist, guided nondominated sorting procedure, developed for solving many-objectives problems. Using a new niching-based selection procedure, our proposed unified algorithm automatically degenerates to an efficient equivalent population-based algorithm for each class. No extra parameters are needed. Extensive simulations are performed on unconstrained and constrained test problems having single-, two-, multi-, and many-objectives and on two engineering optimization design problems. Performance of the unified approach is compared to suitable population-based counterparts at each dimensional level. Results amply demonstrate the merit of our proposed unified approach and motivate similar studies for a richer understanding of the development of optimization algorithms. |
| Author | Seada, Haitham Deb, Kalyanmoy |
| Author_xml | – sequence: 1 givenname: Haitham surname: Seada fullname: Seada, Haitham email: seadahai@msu.edu organization: Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA – sequence: 2 givenname: Kalyanmoy surname: Deb fullname: Deb, Kalyanmoy email: kdeb@msu.edu organization: Dept. of Comput. Sci. & Eng., Michigan State Univ., East Lansing, MI, USA |
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| SubjectTerms | Algorithm design and analysis Algorithms Design engineering Design optimization Equivalence Evolutionary Evolutionary algorithms Heuristic algorithms mono-objective optimization NSGA-III Objectives Optimization Optimization algorithms Representations Sociology Software Software algorithms Statistics Unified algorithms |
| Title | A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives |
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