Multi-objective quantum evolutionary algorithm for discrete multi-objective combinational problem

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

4 Citations (Scopus)

Abstract

Weighted linear scalar, which transfers a multi-objective problem to many single objective sub-problems, is a basic strategy in traditional multi-objective optimization. However, it is not well used in many multi-objective evolutionary algorithms because of most of them are lack of balancing between exploitation and exploration for all sub-problems. This paper proposes a novel multi-objective evolutionary algorithm called multi-objective quantum evolutionary algorithm (MOQEA). Quantum evolutionary algorithm is a recent developed heuristic algorithm, based on the concept of quantum computing. The most merit of QEA is that it has little q-bit individuals are evolved to obtain an acceptable result. MOQEA decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems and optimizes them simultaneously. Each sub-problem is optimized by one q-bit individual. The neighboring solutions that are defined as a set of non-dominated solutions of sub-problem are generated from the corresponding q-bit individual. The experimental results have demonstrated that MOQEA outperforms or performs similarly to MOGLS and NSGA-II on discrete multi-objective problems.

Original languageEnglish
Title of host publicationProceedings - International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010
Pages39-46
Number of pages8
DOIs
Publication statusPublished - 2010 Dec 1
Event2010 15th Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010 - Hsinchu, Taiwan, Province of China
Duration: 2010 Nov 182010 Nov 20

Publication series

NameProceedings - International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010

Conference

Conference2010 15th Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010
CountryTaiwan, Province of China
CityHsinchu
Period10/11/1810/11/20

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Keywords

  • Evolutionary algorithm
  • Multi-objective optimization
  • Pareto front
  • Quantum evolutionary algorithm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

Cite this

Wei, X., & Fujimura, S. (2010). Multi-objective quantum evolutionary algorithm for discrete multi-objective combinational problem. In Proceedings - International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010 (pp. 39-46). [5695430] (Proceedings - International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010). https://doi.org/10.1109/TAAI.2010.18