Exploiting the marginal profits of constraints with evolutionary multi-objective optimization techniques

Yan Zhenyu, Zhi Wei, Kang Lishan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many real-world search and optimization problems naturally involve constraint handling. Recently, quite a few heuristic methods were proposed to solve the nonlinear constrained optimization problems. However, the constraint-handling approaches in these methods have some drawbacks. In this paper, we gave a Multi-objective optimization problem based (MOP-based) formula for constrained single-objective optimization problems. We proposed a way to solve them by using multi-objective evolutionary algorithms (MOEAs). By simulation experiments, we find this approach for constraint handling not only can find the constrained optimally, but also can provide the decision maker (DM) with a group of trade-off solutions with slightly constraint violation and meanwhile with substantial gain in the objective function. This can enable the DM to have more freedom to choose his preferred solution and therefore exploit more profits in the margin of constraint violations, where the constraint violations are small or acceptable.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Artificial Intelligence IC-AI 2003
EditorsH.R. Arabnia, R. Joshua, Y. Mun, H.R. Arabnia, R. Joshua, Y. Mun
Pages251-256
Number of pages6
StatePublished - Dec 1 2003
Externally publishedYes
EventProceedings of the International Conference on Artificial Intelligence, IC-AI 2003 - Las Vegas, NV, United States
Duration: Jun 23 2003Jun 26 2003

Publication series

NameProceedings of the International Conference on Artificial Intelligence IC-AI 2003
Volume1

Other

OtherProceedings of the International Conference on Artificial Intelligence, IC-AI 2003
CountryUnited States
CityLas Vegas, NV
Period6/23/036/26/03

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Keywords

  • Constrained optimization problems
  • Constraint handling
  • Decision making
  • Evolutionary multi-objective optimization

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