Soft Computing

, Volume 21, Issue 24, pp 7435–7445 | Cite as

Many-objective optimization with dynamic constraint handling for constrained optimization problems

Methodologies and Application
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Abstract

In real-world applications, the optimization problems are usually subject to various constraints. To solve constrained optimization problems (COPs), this paper presents a new methodology, which incorporates a dynamic constraint handling mechanism into many-objective evolutionary optimization. Firstly we convert a COP into a dynamic constrained many-objective optimization problem (DCMaOP), which is equivalent to the COP, then the proposed many-objective optimization evolutionary algorithm with dynamic constraint handling, called MaDC, is realized to solve the DCMaOP. MaDC uses the differential evolution (DE) to generate individuals, and a reference-point-based nondominated sorting approach to select individuals. The effectiveness of MaDC is verified on 22 test instances. The experimental results show that MaDC is competitive to several state-of-the-art algorithms, and it has better global search ability than its peer algorithms.

Keywords

Constrained optimization Many-objective optimization Dynamic constraint optimization Reference-point-based nondominated sorting 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Computer ScienceChina University of GeosciencesWuhanPeople’s Republic of China
  2. 2.School of Information EngineeringShijiazhuang University of EconomicsShijiazhuangPeople’s Republic of China

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