Multiobjective weapon-Target assignment problem by two-stage evolutionary multiobjective particle swarm optimization

Deyun Zhou; Xiaoyang Li; Qian Pan; Kun Zhang; Lina Zeng

doi:10.1109/ICInfA.2016.7831950

Multiobjective weapon-Target assignment problem by two-stage evolutionary multiobjective particle swarm optimization

Deyun Zhou, Xiaoyang Li, Qian Pan, Kun Zhang, Lina Zeng

School of Electronics and Information

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

This paper presents a two-stage evolutionary multiobjective particle swarm optimization (TSMOPSO) algorithm to solve the multiobjective weapon-Target assignment (WTA) problem. In order to improve the convergence and the rate of convergence of the proposed algorithm, a two-stage evolutionary strategy is employed. In the first stage of evolution, the population is evolved by the particle status updating rules defined in particle swarm algorithm (PSO). In the second stage of evolution, a novel evolution operator is designed to update the Pareto front solutions which is obtained in the first stage of evolution. Experiments indicate that TSMOPSO algorithm is an effective method to solve the multiobjective weapon-Target assignment problem, and the convergence and the rate of convergence of this algorithm have an obvious improvement.

Original language	English
Title of host publication	2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	921-926
Number of pages	6
ISBN (Electronic)	9781509041022
DOIs	https://doi.org/10.1109/ICInfA.2016.7831950
State	Published - 24 Jan 2017
Event	2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016 - Ningbo, China Duration: 1 Aug 2016 → 3 Aug 2016

Publication series

Name	2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016

Conference

Conference	2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016
Country/Territory	China
City	Ningbo
Period	1/08/16 → 3/08/16

Keywords

Evolutionary multiobjective optimization
Multiobjective optimization
Multiobjective particle swarm optimization
Weapon-Target assignment

Access to Document

10.1109/ICInfA.2016.7831950

Cite this

Zhou, D., Li, X., Pan, Q., Zhang, K., & Zeng, L. (2017). Multiobjective weapon-Target assignment problem by two-stage evolutionary multiobjective particle swarm optimization. In 2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016 (pp. 921-926). Article 7831950 (2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICInfA.2016.7831950

Zhou, Deyun ; Li, Xiaoyang ; Pan, Qian et al. / Multiobjective weapon-Target assignment problem by two-stage evolutionary multiobjective particle swarm optimization. 2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 921-926 (2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016).

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title = "Multiobjective weapon-Target assignment problem by two-stage evolutionary multiobjective particle swarm optimization",

abstract = "This paper presents a two-stage evolutionary multiobjective particle swarm optimization (TSMOPSO) algorithm to solve the multiobjective weapon-Target assignment (WTA) problem. In order to improve the convergence and the rate of convergence of the proposed algorithm, a two-stage evolutionary strategy is employed. In the first stage of evolution, the population is evolved by the particle status updating rules defined in particle swarm algorithm (PSO). In the second stage of evolution, a novel evolution operator is designed to update the Pareto front solutions which is obtained in the first stage of evolution. Experiments indicate that TSMOPSO algorithm is an effective method to solve the multiobjective weapon-Target assignment problem, and the convergence and the rate of convergence of this algorithm have an obvious improvement.",

keywords = "Evolutionary multiobjective optimization, Multiobjective optimization, Multiobjective particle swarm optimization, Weapon-Target assignment",

author = "Deyun Zhou and Xiaoyang Li and Qian Pan and Kun Zhang and Lina Zeng",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016 ; Conference date: 01-08-2016 Through 03-08-2016",

year = "2017",

month = jan,

day = "24",

doi = "10.1109/ICInfA.2016.7831950",

language = "英语",

series = "2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016",

}

Zhou, D, Li, X, Pan, Q, Zhang, K & Zeng, L 2017, Multiobjective weapon-Target assignment problem by two-stage evolutionary multiobjective particle swarm optimization. in 2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016., 7831950, 2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016, Institute of Electrical and Electronics Engineers Inc., pp. 921-926, 2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016, Ningbo, China, 1/08/16. https://doi.org/10.1109/ICInfA.2016.7831950

Multiobjective weapon-Target assignment problem by two-stage evolutionary multiobjective particle swarm optimization. / Zhou, Deyun; Li, Xiaoyang; Pan, Qian et al.
2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 921-926 7831950 (2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multiobjective weapon-Target assignment problem by two-stage evolutionary multiobjective particle swarm optimization

AU - Zhou, Deyun

AU - Li, Xiaoyang

AU - Pan, Qian

AU - Zhang, Kun

AU - Zeng, Lina

PY - 2017/1/24

Y1 - 2017/1/24

N2 - This paper presents a two-stage evolutionary multiobjective particle swarm optimization (TSMOPSO) algorithm to solve the multiobjective weapon-Target assignment (WTA) problem. In order to improve the convergence and the rate of convergence of the proposed algorithm, a two-stage evolutionary strategy is employed. In the first stage of evolution, the population is evolved by the particle status updating rules defined in particle swarm algorithm (PSO). In the second stage of evolution, a novel evolution operator is designed to update the Pareto front solutions which is obtained in the first stage of evolution. Experiments indicate that TSMOPSO algorithm is an effective method to solve the multiobjective weapon-Target assignment problem, and the convergence and the rate of convergence of this algorithm have an obvious improvement.

AB - This paper presents a two-stage evolutionary multiobjective particle swarm optimization (TSMOPSO) algorithm to solve the multiobjective weapon-Target assignment (WTA) problem. In order to improve the convergence and the rate of convergence of the proposed algorithm, a two-stage evolutionary strategy is employed. In the first stage of evolution, the population is evolved by the particle status updating rules defined in particle swarm algorithm (PSO). In the second stage of evolution, a novel evolution operator is designed to update the Pareto front solutions which is obtained in the first stage of evolution. Experiments indicate that TSMOPSO algorithm is an effective method to solve the multiobjective weapon-Target assignment problem, and the convergence and the rate of convergence of this algorithm have an obvious improvement.

KW - Evolutionary multiobjective optimization

KW - Multiobjective optimization

KW - Multiobjective particle swarm optimization

KW - Weapon-Target assignment

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Zhou D, Li X, Pan Q, Zhang K, Zeng L. Multiobjective weapon-Target assignment problem by two-stage evolutionary multiobjective particle swarm optimization. In 2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 921-926. 7831950. (2016 IEEE International Conference on Information and Automation, IEEE ICIA 2016). doi: 10.1109/ICInfA.2016.7831950

Multiobjective weapon-Target assignment problem by two-stage evolutionary multiobjective particle swarm optimization

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