Parallel quantum evolutionary algorithms with client-server model for multi-objective optimization on discrete problems

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

1 Citation (Scopus)

Abstract

This paper proposes a parallel quantum evolutionary algorithm (PQEA) using Client-Server model for multi-objective optimization problems. Firstly, the PQEA uniformly decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems. All the sub-problems are classified into several groups according to their similarities. Each "Client" processes the evolution for a group of neighbor sub-problems in parallel. There is a quantum individual used to address the sub-problems of a group in a "Client". Since the quantum individual is a probabilistic representation, it can share evolutionary information of the neighbor sub-problems in one group, while the sub-problems are orderly solved using a same q-bit individual. The "Server" maintains non-dominated solutions that are generated by every "Client". The current best solution for each sub-problem can be found in the "Server", when the quantum individual updated its states for evolution. Experimental results have demonstrated that PQEA obviously outperforms the most famous multi-objective optimization algorithms MOEA/D on the bi-objectives. For the more objectives, the PQEA obtains the similar results with MOEA/D, even with the same evaluation times. Furthermore, in this paper, the scalability and sensitivity of PQEA have also been experimentally investigated.

Original languageEnglish
Title of host publication2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs
Publication statusPublished - 2012 Oct 4
Event2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
Duration: 2012 Jun 102012 Jun 15

Publication series

Name2012 IEEE Congress on Evolutionary Computation, CEC 2012

Conference

Conference2012 IEEE Congress on Evolutionary Computation, CEC 2012
CountryAustralia
CityBrisbane, QLD
Period12/6/1012/6/15

Keywords

  • Multi-Objective Optimization
  • Parallel
  • Pareto Front
  • Quantum Evolutionary Algorithm

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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  • Cite this

    Wei, X., & Fujimura, S. (2012). Parallel quantum evolutionary algorithms with client-server model for multi-objective optimization on discrete problems. In 2012 IEEE Congress on Evolutionary Computation, CEC 2012 [6252958] (2012 IEEE Congress on Evolutionary Computation, CEC 2012). https://doi.org/10.1109/CEC.2012.6252958