Coevolutionary Multiobjective Evolutionary Algorithms: Survey of the State-of-the-Art

In the last 20 years, evolutionary algorithms (EAs) have shown to be an effective method to solve multiobjective optimization problems (MOPs). Due to their population-based nature, multiobjective EAs (MOEAs) are able to generate a set of tradeoff solutions (called nondominated solutions) in a single...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 22; číslo 6; s. 851 - 865
Hlavní autoři: Miguel Antonio, Luis, Coello Coello, Carlos A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Coevolutionary algorithms (CAs)
competitive coevolution
Computer science
cooperative coevolution (CC)
Evolutionary algorithms
Evolutionary computation
Formulations
Genetic algorithms
Global optimization
Mathematical analysis
Mathematical programming
Mopping
multiobjective optimization
Multiple objective analysis
Pareto optimization
Sociology
Taxonomy
ISSN:1089-778X, 1941-0026
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Shrnutí:In the last 20 years, evolutionary algorithms (EAs) have shown to be an effective method to solve multiobjective optimization problems (MOPs). Due to their population-based nature, multiobjective EAs (MOEAs) are able to generate a set of tradeoff solutions (called nondominated solutions) in a single algorithmic execution instead of having to perform a series of independent executions, as normally done with mathematical programming techniques. Additionally, MOEAs can be successfully applied to problems with difficult features such as multifrontality, discontinuity and disjoint feasible regions, among others. On the other hand, coevolutionary algorithms (CAs) are extensions of traditional EAs which have become subject of numerous studies in the last few years, particularly for dealing with large-scale global optimization problems. CAs have also been applied to the solution of MOPs, motivating the development of new algorithmic and analytical formulations that have advanced the state-of-the-art in CAs research, while simultaneously opening a new research path within MOEAs. This paper presents a critical review of the most representative coevolutionary MOEAs (CMOEAs) that have been reported in the specialized literature. This survey includes a taxonomy of approaches together with a brief description of their main features. In the final part of this paper, we also identify what we believe to be promising areas of future research in the field of CMOEAs.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2767023