Multiupdate mode quantum evolutionary algorithm and its applications to combination and permutation problems

Research output: Contribution to journalArticle

Abstract

Based on the concept and principles of quantum computing, this paper proposes a new evolutionary algorithm called multiupdate mode quantum evolutionary algorithm (MMQEA). MMQEA, like the other classic evolutionary algorithms, is also characterized by the representation of the individual evaluation function and the population dynamics; however, instead of binary, numeric, or symbolic representation, MMQEA uses two interactional q-bit strings as an individual. Update modes are introduced as a variational operation that evolves the individuals to make a better solution. The proposed individual structure and update modes are inspired by quantum entanglement. Update modes perform as reproducing the states of a pair of q-bit strings of individual simultaneously. For guiding the individual evolution to maintain the population diversity and avoid prematurity, each q-bit string of individual provides its evolutionary history information to another. To demonstrate its effectiveness and applicability, the proposed algorithm was tested on two famous combinatorial optimization problems, namely, the knapsack problem and flow shop problem. The results show that MMQEA performs very well compared to quantum evolutionary algorithm (QEA) and the conventional genetic algorithm.

Original languageEnglish
Pages (from-to)166-173
Number of pages8
JournalIEEJ Transactions on Electrical and Electronic Engineering
Volume7
Issue number2
DOIs
Publication statusPublished - 2012 Mar

Fingerprint

Evolutionary algorithms
Quantum entanglement
Population dynamics
Function evaluation
Combinatorial optimization
Genetic algorithms
History

Keywords

  • Flowshop problem
  • Knapsack problem
  • Multiupdate mode
  • Q-bit
  • Quantum evolutionary algorithm

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

@article{d432e732a3bf40ca9fbcfc2ba28c6b97,
title = "Multiupdate mode quantum evolutionary algorithm and its applications to combination and permutation problems",
abstract = "Based on the concept and principles of quantum computing, this paper proposes a new evolutionary algorithm called multiupdate mode quantum evolutionary algorithm (MMQEA). MMQEA, like the other classic evolutionary algorithms, is also characterized by the representation of the individual evaluation function and the population dynamics; however, instead of binary, numeric, or symbolic representation, MMQEA uses two interactional q-bit strings as an individual. Update modes are introduced as a variational operation that evolves the individuals to make a better solution. The proposed individual structure and update modes are inspired by quantum entanglement. Update modes perform as reproducing the states of a pair of q-bit strings of individual simultaneously. For guiding the individual evolution to maintain the population diversity and avoid prematurity, each q-bit string of individual provides its evolutionary history information to another. To demonstrate its effectiveness and applicability, the proposed algorithm was tested on two famous combinatorial optimization problems, namely, the knapsack problem and flow shop problem. The results show that MMQEA performs very well compared to quantum evolutionary algorithm (QEA) and the conventional genetic algorithm.",
keywords = "Flowshop problem, Knapsack problem, Multiupdate mode, Q-bit, Quantum evolutionary algorithm",
author = "Xin Wei and Shigeru Fujimura",
year = "2012",
month = "3",
doi = "10.1002/tee.21712",
language = "English",
volume = "7",
pages = "166--173",
journal = "IEEJ Transactions on Electrical and Electronic Engineering",
issn = "1931-4973",
publisher = "John Wiley and Sons Inc.",
number = "2",

}

TY - JOUR

T1 - Multiupdate mode quantum evolutionary algorithm and its applications to combination and permutation problems

AU - Wei, Xin

AU - Fujimura, Shigeru

PY - 2012/3

Y1 - 2012/3

N2 - Based on the concept and principles of quantum computing, this paper proposes a new evolutionary algorithm called multiupdate mode quantum evolutionary algorithm (MMQEA). MMQEA, like the other classic evolutionary algorithms, is also characterized by the representation of the individual evaluation function and the population dynamics; however, instead of binary, numeric, or symbolic representation, MMQEA uses two interactional q-bit strings as an individual. Update modes are introduced as a variational operation that evolves the individuals to make a better solution. The proposed individual structure and update modes are inspired by quantum entanglement. Update modes perform as reproducing the states of a pair of q-bit strings of individual simultaneously. For guiding the individual evolution to maintain the population diversity and avoid prematurity, each q-bit string of individual provides its evolutionary history information to another. To demonstrate its effectiveness and applicability, the proposed algorithm was tested on two famous combinatorial optimization problems, namely, the knapsack problem and flow shop problem. The results show that MMQEA performs very well compared to quantum evolutionary algorithm (QEA) and the conventional genetic algorithm.

AB - Based on the concept and principles of quantum computing, this paper proposes a new evolutionary algorithm called multiupdate mode quantum evolutionary algorithm (MMQEA). MMQEA, like the other classic evolutionary algorithms, is also characterized by the representation of the individual evaluation function and the population dynamics; however, instead of binary, numeric, or symbolic representation, MMQEA uses two interactional q-bit strings as an individual. Update modes are introduced as a variational operation that evolves the individuals to make a better solution. The proposed individual structure and update modes are inspired by quantum entanglement. Update modes perform as reproducing the states of a pair of q-bit strings of individual simultaneously. For guiding the individual evolution to maintain the population diversity and avoid prematurity, each q-bit string of individual provides its evolutionary history information to another. To demonstrate its effectiveness and applicability, the proposed algorithm was tested on two famous combinatorial optimization problems, namely, the knapsack problem and flow shop problem. The results show that MMQEA performs very well compared to quantum evolutionary algorithm (QEA) and the conventional genetic algorithm.

KW - Flowshop problem

KW - Knapsack problem

KW - Multiupdate mode

KW - Q-bit

KW - Quantum evolutionary algorithm

UR - http://www.scopus.com/inward/record.url?scp=84856263137&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84856263137&partnerID=8YFLogxK

U2 - 10.1002/tee.21712

DO - 10.1002/tee.21712

M3 - Article

VL - 7

SP - 166

EP - 173

JO - IEEJ Transactions on Electrical and Electronic Engineering

JF - IEEJ Transactions on Electrical and Electronic Engineering

SN - 1931-4973

IS - 2

ER -