A quantum-inspired evolutionary algorithm for multi-objective design

Siu Lau Ho, Shiyou Yang, Peihong Ni, Jin Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


To explore the full potential of Quantum-inspired Evolutionary Algorithms (QEA) in multiobjective design optimizations, a vector QEA is proposed. To fulfill the two ultimate goals of a vector optimizer in finding and uniformly sampling the Pareto front of a multi-objective inverse problem, a fitness assignment formula to consider the number of improvements in the whole objective functions and the amount of the improvement in a specified objective function, as well as the use of a selection mechanism in choosing the so far searched best solutions, are proposed in this paper. The information sharing and the increment angle updating components of the scalar QEA have also been redesigned according to the characteristics of multi-objective inverse problems. Numerical results on two case studies are presented to validate the proposed vector QEA.
Original languageEnglish
Article number6514783
Pages (from-to)1609-1612
Number of pages4
JournalIEEE Transactions on Magnetics
Issue number5
Publication statusPublished - 22 May 2013


  • Evolutionary algorithm
  • inverse problem
  • multi-objective optimization
  • quantum computing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering


Dive into the research topics of 'A quantum-inspired evolutionary algorithm for multi-objective design'. Together they form a unique fingerprint.

Cite this