A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem

The weapon-target assignment (WTA) problem, known as an NP-complete problem, aims at seeking a proper assignment of weapons to targets. The biobjective WTA (BOWTA) optimization model which maximizes the expected damage of the enemy and minimizes the cost of missiles is designed in this paper. A modi...

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Main Authors: You Li, Yingxin Kou, Zhanwu Li, An Xu, Yizhe Chang
Format: Article
Language:English
Published: Wiley 2017-01-01
Series:International Journal of Aerospace Engineering
Online Access:http://dx.doi.org/10.1155/2017/1746124
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author You Li
Yingxin Kou
Zhanwu Li
An Xu
Yizhe Chang
author_facet You Li
Yingxin Kou
Zhanwu Li
An Xu
Yizhe Chang
author_sort You Li
collection DOAJ
description The weapon-target assignment (WTA) problem, known as an NP-complete problem, aims at seeking a proper assignment of weapons to targets. The biobjective WTA (BOWTA) optimization model which maximizes the expected damage of the enemy and minimizes the cost of missiles is designed in this paper. A modified Pareto ant colony optimization (MPACO) algorithm is used to solve the BOWTA problem. In order to avoid defects in traditional optimization algorithms and obtain a set of Pareto solutions efficiently, MPACO algorithm based on new designed operators is proposed, including a dynamic heuristic information calculation approach, an improved movement probability rule, a dynamic evaporation rate strategy, a global updating rule of pheromone, and a boundary symmetric mutation strategy. In order to simulate real air combat, the pilot operation factor is introduced into the BOWTA model. Finally, we apply the MPACO algorithm and other algorithms to the model and compare the data. Simulation results show that the proposed algorithm is successfully applied in the field of WTA which improves the performance of the traditional P-ACO algorithm effectively and produces better solutions than the two well-known multiobjective optimization algorithms NSGA-II and SPEA-II.
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institution Kabale University
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spelling doaj-art-3964eb025aae424db175ff7be94c31b92025-08-20T03:55:12ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742017-01-01201710.1155/2017/17461241746124A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment ProblemYou Li0Yingxin Kou1Zhanwu Li2An Xu3Yizhe Chang4Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaAeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaAeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaAeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaAeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaThe weapon-target assignment (WTA) problem, known as an NP-complete problem, aims at seeking a proper assignment of weapons to targets. The biobjective WTA (BOWTA) optimization model which maximizes the expected damage of the enemy and minimizes the cost of missiles is designed in this paper. A modified Pareto ant colony optimization (MPACO) algorithm is used to solve the BOWTA problem. In order to avoid defects in traditional optimization algorithms and obtain a set of Pareto solutions efficiently, MPACO algorithm based on new designed operators is proposed, including a dynamic heuristic information calculation approach, an improved movement probability rule, a dynamic evaporation rate strategy, a global updating rule of pheromone, and a boundary symmetric mutation strategy. In order to simulate real air combat, the pilot operation factor is introduced into the BOWTA model. Finally, we apply the MPACO algorithm and other algorithms to the model and compare the data. Simulation results show that the proposed algorithm is successfully applied in the field of WTA which improves the performance of the traditional P-ACO algorithm effectively and produces better solutions than the two well-known multiobjective optimization algorithms NSGA-II and SPEA-II.http://dx.doi.org/10.1155/2017/1746124
spellingShingle You Li
Yingxin Kou
Zhanwu Li
An Xu
Yizhe Chang
A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem
International Journal of Aerospace Engineering
title A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem
title_full A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem
title_fullStr A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem
title_full_unstemmed A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem
title_short A Modified Pareto Ant Colony Optimization Approach to Solve Biobjective Weapon-Target Assignment Problem
title_sort modified pareto ant colony optimization approach to solve biobjective weapon target assignment problem
url http://dx.doi.org/10.1155/2017/1746124
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