Improved version of a multiobjective quantum-inspired evolutionary algorithm with preference-based selection

Multiobjective quantum-inspired evolutionary algorithm (MQEA) employs Q-bit individuals, which are updated using rotation gate by referring to nondominated solutions in an archive. In this way, a population can quickly converge to the Pareto optimal solution set. To obtain the specific solutions bas...

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Published in:2012 IEEE Congress on Evolutionary Computation pp. 1 - 7
Main Authors: Si-Jung Ryu, Ki-Baek Lee, Jong-Hwan Kim
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2012
Subjects:
Equations
Evolutionary computation
Optimization
Power measurement
Probabilistic logic
Quantum computing
Sorting
ISBN:1467315109, 9781467315104
ISSN:1089-778X
Online Access:Get full text
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Abstract Multiobjective quantum-inspired evolutionary algorithm (MQEA) employs Q-bit individuals, which are updated using rotation gate by referring to nondominated solutions in an archive. In this way, a population can quickly converge to the Pareto optimal solution set. To obtain the specific solutions based on user's preference in the population, MQEA with preference-based selection (MQEA-PS) is developed. In this paper, an improved version of MQEA-PS, MQEA-PS2, is proposed, where global population is sorted and divided into groups, upper half of individuals in each group are selected by global evaluation, and selected solutions are globally migrated. The global evaluation of nondominated solutions is performed by the fuzzy integral of partial evaluation with respect to the fuzzy measures, where the partial evaluation value is obtained from a normalized objective function value. To demonstrate the effectiveness of the proposed MQEA-PS2, comparisons with MQEA and MQEA-PS are carried out for DTLZ functions.
AbstractList Multiobjective quantum-inspired evolutionary algorithm (MQEA) employs Q-bit individuals, which are updated using rotation gate by referring to nondominated solutions in an archive. In this way, a population can quickly converge to the Pareto optimal solution set. To obtain the specific solutions based on user's preference in the population, MQEA with preference-based selection (MQEA-PS) is developed. In this paper, an improved version of MQEA-PS, MQEA-PS2, is proposed, where global population is sorted and divided into groups, upper half of individuals in each group are selected by global evaluation, and selected solutions are globally migrated. The global evaluation of nondominated solutions is performed by the fuzzy integral of partial evaluation with respect to the fuzzy measures, where the partial evaluation value is obtained from a normalized objective function value. To demonstrate the effectiveness of the proposed MQEA-PS2, comparisons with MQEA and MQEA-PS are carried out for DTLZ functions.
Author Si-Jung Ryu
Ki-Baek Lee
Jong-Hwan Kim
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  surname: Si-Jung Ryu
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  surname: Ki-Baek Lee
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  surname: Jong-Hwan Kim
  fullname: Jong-Hwan Kim
  email: johkim@rit.kaist.ac.kr
  organization: Dept. of Electr. Eng., KAIST, Daejeon, South Korea
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Snippet Multiobjective quantum-inspired evolutionary algorithm (MQEA) employs Q-bit individuals, which are updated using rotation gate by referring to nondominated...
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StartPage 1
SubjectTerms Equations
Evolutionary computation
Optimization
Power measurement
Probabilistic logic
Quantum computing
Sorting
Title Improved version of a multiobjective quantum-inspired evolutionary algorithm with preference-based selection
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