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Multiobjective Quantum-Inspired Evolutionary Algorithm with Preference-Based Selection 2: Comparison Study

  • Si-Jung Ryu
  • Ki-Baek Lee
  • Bum-Soo Yoo
  • Tae-Jin Kim
  • Seung-Jae Lee
  • Jong-Hwan Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7429)

Abstract

This paper proposes an improved version of multiobjective quantum-inspired evolutionary algorithm with preference-based selection (MQEA-PS2). Unlike MQEA-PS, global population is sorted and divided into groups, and then upper half individuals in each group are selected by global evaluation and globally migrated to subpopulations in the MQEA-PS2. Fuzzy integral is employed for global evaluation of the individuals. By this procedure, reference populations contain not only the most preferred solution, but also less preferred solutions because individuals with various global evaluation values are migrated to the reference populations. This leads to an improvement of performance, especially the diversity for the optimization problems. To demonstrate the effectiveness of the proposed MQEA-PS2, comparisons with MQEA and MQEA-PS are carried out for five ZDT functions.

Keywords

Multi-Objective Evolutionary Algorithm Multiobjective Quantum-inspired Evolutionary Algorithm Fuzzy Integral Fuzzy Measure Preference-based Solution Selection Algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Si-Jung Ryu
    • 1
  • Ki-Baek Lee
    • 1
  • Bum-Soo Yoo
    • 1
  • Tae-Jin Kim
    • 1
  • Seung-Jae Lee
    • 1
  • Jong-Hwan Kim
    • 1
  1. 1.Department of Electrical EngineeringKAISTDaejeonRepublic of Korea

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