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|Title:||A two-phase evolutionary algorithm framework for multi-objective optimization||Authors:||Jiang, S.
|Keywords:||Engineering::Computer science and engineering||Issue Date:||2021||Source:||Jiang, S. & Chen, Z. (2021). A two-phase evolutionary algorithm framework for multi-objective optimization. Applied Intelligence, 51, 3952-3974. https://dx.doi.org/10.1007/s10489-020-01988-7||Journal:||Applied Intelligence||Abstract:||This paper proposes a two-phase evolutionary algorithm framework for solving multi-objective optimization problems (MOPs), which allows different users to flexibly handle MOPs with different existing algorithms. In the first phase, a specific multi-objective evolutionary algorithm (MOEA) with a smaller population size is adopted to fast obtain a population converging to the true Pareto front. Then, in the second phase, a simple environmental selection mechanism based on a measure function and a well-designed crowdedness function is used to promote the uniformity of population in the objective space. Based on the proposed framework, we form four instantiations by embedding four distinct MOEAs into the first phase of the proposed framework. In the experimental study, different experiments are conducted on a variety of well-known benchmark problems from 3 to 10 objectives, and experimental results demonstrate the effect of the proposed framework. Furthermore, compared with several state-of-the-art multi-objective evolutionary algorithms, the four instantiations of the proposed framework have better performance and can obtain well-distributed solution sets. In short, the proposed framework has the strong ability to promote the performance of existing algorithms.||URI:||https://hdl.handle.net/10356/154500||ISSN:||0924-669X||DOI:||10.1007/s10489-020-01988-7||Rights:||© 2020 Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
Updated on May 20, 2022
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