An improved partheno-genetic algorithm for the multi-constrained problem of curling match arrangement

Rui Ding; Hongbin Dong; Jun He; Yuxin Dong

doi:10.1109/CEC.2016.7743893

An improved partheno-genetic algorithm for the multi-constrained problem of curling match arrangement

Rui Ding
, Hongbin Dong
, Jun He
, Yuxin Dong

Adran Cyfrifiadureg

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (ISBN)

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Curling-match arrangement is a multi-constrained optimization problem in the real world. An improved partheno-genetic algorithm is used for solving the problem in this paper. In order to handle the complicated relationships among the particular constraints in curling-match, an eliminate-selection strategy is proposed to increase population diversity. Two genetic operators, targeted self-crossover operator and fixed-random self-crossover operator, are designed to ensure that the algorithm can convergence rapidly. With bi-level optimization, the improved partheno-genetic algorithm enhances its search ability. An orthogonal method is used to obtain the algorithm parameters. Simulation results demonstrate that the improved algorithm can solve the curling-match multi-constrained optimization problem efficiently.

Iaith wreiddiol	Saesneg
Teitl	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Cyhoeddwr	IEEE Press
Tudalennau	957-964
Nifer y tudalennau	8
ISBN (Electronig)	9781509006229
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1109/CEC.2016.7743893
Statws	Cyhoeddwyd - 14 Tach 2016
Digwyddiad	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Hyd: 24 Gorff 2016 → 29 Gorff 2016

Cyfres gyhoeddiadau

Enw	2016 IEEE Congress on Evolutionary Computation, CEC 2016

Cynhadledd

Cynhadledd	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Gwlad/Tiriogaeth	Canada
Dinas	Vancouver
Cyfnod	24 Gorff 2016 → 29 Gorff 2016

Mynediad at Ddogfen

10.1109/CEC.2016.7743893

Cysylltiad parhaol

Dolen barhaus

Ffeiliau eraill a chysylltiadau

Link to publication in Scopus

Dyfynnu hyn

@inproceedings{e0c21cc776694f64bb4a40d5fe7ca1f3,

title = "An improved partheno-genetic algorithm for the multi-constrained problem of curling match arrangement",

abstract = "Curling-match arrangement is a multi-constrained optimization problem in the real world. An improved partheno-genetic algorithm is used for solving the problem in this paper. In order to handle the complicated relationships among the particular constraints in curling-match, an eliminate-selection strategy is proposed to increase population diversity. Two genetic operators, targeted self-crossover operator and fixed-random self-crossover operator, are designed to ensure that the algorithm can convergence rapidly. With bi-level optimization, the improved partheno-genetic algorithm enhances its search ability. An orthogonal method is used to obtain the algorithm parameters. Simulation results demonstrate that the improved algorithm can solve the curling-match multi-constrained optimization problem efficiently.",

keywords = "Curling Match Arrangement, Eliminate-selection Strategy, Fixed-Random Self-Crossover, Partheno-Genetic Algorithm, Targeted Self-Crossover",

author = "Rui Ding and Hongbin Dong and Jun He and Yuxin Dong",

year = "2016",

month = nov,

day = "14",

doi = "10.1109/CEC.2016.7743893",

language = "English",

series = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",

publisher = "IEEE Press",

pages = "957--964",

booktitle = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",

address = "United States of America",

note = "2016 IEEE Congress on Evolutionary Computation, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

}

Ding, R, Dong, H, He, J & Dong, Y 2016, An improved partheno-genetic algorithm for the multi-constrained problem of curling match arrangement. yn 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7743893, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, IEEE Press, tt. 957-964, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 24 Gorff 2016. https://doi.org/10.1109/CEC.2016.7743893

An improved partheno-genetic algorithm for the multi-constrained problem of curling match arrangement. / Ding, Rui; Dong, Hongbin; He, Jun et al.
2016 IEEE Congress on Evolutionary Computation, CEC 2016. IEEE Press, 2016. t. 957-964 7743893 (2016 IEEE Congress on Evolutionary Computation, CEC 2016).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (ISBN)

TY - GEN

T1 - An improved partheno-genetic algorithm for the multi-constrained problem of curling match arrangement

AU - Ding, Rui

AU - Dong, Hongbin

AU - He, Jun

AU - Dong, Yuxin

PY - 2016/11/14

Y1 - 2016/11/14

N2 - Curling-match arrangement is a multi-constrained optimization problem in the real world. An improved partheno-genetic algorithm is used for solving the problem in this paper. In order to handle the complicated relationships among the particular constraints in curling-match, an eliminate-selection strategy is proposed to increase population diversity. Two genetic operators, targeted self-crossover operator and fixed-random self-crossover operator, are designed to ensure that the algorithm can convergence rapidly. With bi-level optimization, the improved partheno-genetic algorithm enhances its search ability. An orthogonal method is used to obtain the algorithm parameters. Simulation results demonstrate that the improved algorithm can solve the curling-match multi-constrained optimization problem efficiently.

AB - Curling-match arrangement is a multi-constrained optimization problem in the real world. An improved partheno-genetic algorithm is used for solving the problem in this paper. In order to handle the complicated relationships among the particular constraints in curling-match, an eliminate-selection strategy is proposed to increase population diversity. Two genetic operators, targeted self-crossover operator and fixed-random self-crossover operator, are designed to ensure that the algorithm can convergence rapidly. With bi-level optimization, the improved partheno-genetic algorithm enhances its search ability. An orthogonal method is used to obtain the algorithm parameters. Simulation results demonstrate that the improved algorithm can solve the curling-match multi-constrained optimization problem efficiently.

KW - Curling Match Arrangement

KW - Eliminate-selection Strategy

KW - Fixed-Random Self-Crossover

KW - Partheno-Genetic Algorithm

KW - Targeted Self-Crossover

UR - https://www.scopus.com/pages/publications/85008263110

U2 - 10.1109/CEC.2016.7743893

DO - 10.1109/CEC.2016.7743893

M3 - Conference Proceeding (ISBN)

AN - SCOPUS:85008263110

T3 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016

SP - 957

EP - 964

BT - 2016 IEEE Congress on Evolutionary Computation, CEC 2016

PB - IEEE Press

T2 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016

Y2 - 24 July 2016 through 29 July 2016

ER -

An improved partheno-genetic algorithm for the multi-constrained problem of curling match arrangement

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