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

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

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.

Original language	English
Title of host publication	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Publisher	IEEE Press
Pages	957-964
Number of pages	8
ISBN (Electronic)	9781509006229
DOIs	https://doi.org/10.1109/CEC.2016.7743893
Publication status	Published - 14 Nov 2016
Event	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Publication series

Name	2016 IEEE Congress on Evolutionary Computation, CEC 2016

Conference

Conference	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/Territory	Canada
City	Vancouver
Period	24 Jul 2016 → 29 Jul 2016

Keywords

Curling Match Arrangement
Eliminate-selection Strategy
Fixed-Random Self-Crossover
Partheno-Genetic Algorithm
Targeted Self-Crossover

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10.1109/CEC.2016.7743893

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@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",

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

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Ding, R, Dong, H, He, J & Dong, Y 2016, An improved partheno-genetic algorithm for the multi-constrained problem of curling match arrangement. in 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7743893, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, IEEE Press, pp. 957-964, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 24 Jul 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. p. 957-964 7743893 (2016 IEEE Congress on Evolutionary Computation, CEC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

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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.

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

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