Solving a kind of high complexity multi-objective problems by a fast algorithm



A fast algorithm is proposed to solve a kind of high complexity multi-objective problems in this paper. It takes advantages of both the orthogonal design method to search evenly, and the statistical optimal method to speed up the computation. It is very suitable for solving high complexity problems, and quickly yields solutions which converge to the Pareto-optimal set with high precision and uniform distribution. Some complicated multi-objective problems are solved by the algorithm and the results show that the algorithm is not only fast but also superior to other MOGAS and MOEAs, such as the currently efficient algorithm SPEA, in terms of the precision, quantity and distribution of solutions.

Key Words

evolutionary algorithms orthogonal design multi-objective optimization Pareto-optimal set 

CLC number

TP 391 


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Copyright information

© Springer 2003

Authors and Affiliations

  1. 1.Department of Computer ScienceChina University of GeoSciencesWuhan, HubeiChina
  2. 2.Department of Computer ScienceZhuzhou Institute of TechnologyZhuzhou, HunanChina
  3. 3.State Key Laboratory of Software EngineeringWuhan UniversityWuhan, HubeiChina

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