An Efficient Conical Area Evolutionary Algorithm for Bi-objective Optimization

Weiqin YING; Xing XU; Yuxiang FENG; Yu WU

doi:10.1587/transfun.E95.A.1420

An Efficient Conical Area Evolutionary Algorithm for Bi-objective Optimization

Weiqin YING, Xing XU, Yuxiang FENG, Yu WU

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A conical area evolutionary algorithm (CAEA) is presented to further improve computational efficiencies of evolutionary algorithms for bi-objective optimization. CAEA partitions the objective space into a number of conical subregions and then solves a scalar subproblem in each subregion that uses a conical area indicator as its scalar objective. The local Pareto optimality of the solution with the minimal conical area in each subregion is proved. Experimental results on bi-objective problems have shown that CAEA offers a significantly higher computational efficiency than the multi-objective evolutionary algorithm based on decomposition (MOEA/D) while CAEA competes well with MOEA/D in terms of solution quality.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E95-A No.8 pp.1420-1425

Publication Date: 2012/08/01

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Online ISSN: 1745-1337

DOI: 10.1587/transfun.E95.A.1420

Type of Manuscript: LETTER

Category: Numerical Analysis and Optimization

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Weiqin YING, Xing XU, Yuxiang FENG, Yu WU, "An Efficient Conical Area Evolutionary Algorithm for Bi-objective Optimization" in IEICE TRANSACTIONS on Fundamentals, vol. E95-A, no. 8, pp. 1420-1425, August 2012, doi: 10.1587/transfun.E95.A.1420.
Abstract: A conical area evolutionary algorithm (CAEA) is presented to further improve computational efficiencies of evolutionary algorithms for bi-objective optimization. CAEA partitions the objective space into a number of conical subregions and then solves a scalar subproblem in each subregion that uses a conical area indicator as its scalar objective. The local Pareto optimality of the solution with the minimal conical area in each subregion is proved. Experimental results on bi-objective problems have shown that CAEA offers a significantly higher computational efficiency than the multi-objective evolutionary algorithm based on decomposition (MOEA/D) while CAEA competes well with MOEA/D in terms of solution quality.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E95.A.1420/_p

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@ARTICLE{e95-a_8_1420,
author={Weiqin YING, Xing XU, Yuxiang FENG, Yu WU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Efficient Conical Area Evolutionary Algorithm for Bi-objective Optimization},
year={2012},
volume={E95-A},
number={8},
pages={1420-1425},
abstract={A conical area evolutionary algorithm (CAEA) is presented to further improve computational efficiencies of evolutionary algorithms for bi-objective optimization. CAEA partitions the objective space into a number of conical subregions and then solves a scalar subproblem in each subregion that uses a conical area indicator as its scalar objective. The local Pareto optimality of the solution with the minimal conical area in each subregion is proved. Experimental results on bi-objective problems have shown that CAEA offers a significantly higher computational efficiency than the multi-objective evolutionary algorithm based on decomposition (MOEA/D) while CAEA competes well with MOEA/D in terms of solution quality.},
keywords={},
doi={10.1587/transfun.E95.A.1420},
ISSN={1745-1337},
month={August},}

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TY - JOUR
TI - An Efficient Conical Area Evolutionary Algorithm for Bi-objective Optimization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1420
EP - 1425
AU - Weiqin YING
AU - Xing XU
AU - Yuxiang FENG
AU - Yu WU
PY - 2012
DO - 10.1587/transfun.E95.A.1420
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E95-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2012
AB - A conical area evolutionary algorithm (CAEA) is presented to further improve computational efficiencies of evolutionary algorithms for bi-objective optimization. CAEA partitions the objective space into a number of conical subregions and then solves a scalar subproblem in each subregion that uses a conical area indicator as its scalar objective. The local Pareto optimality of the solution with the minimal conical area in each subregion is proved. Experimental results on bi-objective problems have shown that CAEA offers a significantly higher computational efficiency than the multi-objective evolutionary algorithm based on decomposition (MOEA/D) while CAEA competes well with MOEA/D in terms of solution quality.
ER -