Adaptive chaos parallel clonal selection algorithm for objective optimization in WTA application

Liang, Hongtao; Kang, Fengju

doi:10.1016/j.ijleo.2015.12.122

Cited by 33 publications

(11 citation statements)

References 14 publications

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“…Due to the complexity of the real combat situation, the model's efficiency would increase, once the issue is simplified and a more accurate answer is achieved. Consequently, in comparison with other assignment model 15,17,19 , a linear assignment model is being presented based on a simplification of the issue. Even if there is a big number of targets and sites, it only takes about 3 s to solve the model by obtaining the information from the experts before the combat with the information from the sensors.…”

Section: Discussionmentioning

confidence: 99%

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Designing a Framework for Target-Site Assignment in Naval Combat Management

2016

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In this study, using operational research techniques, a model has been presented to assess battlefield threat, to prioritise aggressive targets, to evaluate the capability of own sites and the risks of the conflict with the targets, to define conflict scenarios and finally to select the best scenario using an assignment model. The above proceedings were added as an intermediate phase of target-site assignment, called 'deciding the best conflict scenario', to the 'threat assessment' and 'weapon-target assignment' in the naval combat management system. For each of the own site, the data collected from the environment together with the panels of experts are shown in a two-dimensional matrix, in which the four areas of the matrix represent the conflict scenarios. Considering that the study was done in a simulated environment, the expert's verification and the convergence of the results in Monte Carlo method were used to validate the research. The proposed model can offer optimised decision to the operational commander through predicting the battlefield and managing the site's capacity and the interaction in between during the combat.

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Section: Discussionmentioning

confidence: 99%

“…the target-site assignment. and the relevant methods solving them, numerous studies have been done [13][14][15][16][17][18] . Ahner & Parson 19 have submitted a model for WTA, by using dynamic programming and stochastic process approach.…”

Section: Introductionmentioning

confidence: 99%

Designing a Framework for Target-Site Assignment in Naval Combat Management

2016

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“…Lee et al [6] 2002 IS + ACO Static single objective Lee et al [7] 2002 GA Static single objective Lee et al [8] 2003 GA + ACO Static single objective Galati and Simaan [5] 2007 Tabu Dynamic single objective Lee [9] 2010 VLSN Static single objective Xin et al [10] 2010 VP + tabu Dynamic single objective Li and Dong [11] 2010 DPSO + SA Dynamic single objective Chen et al [12] 2010 SA Static single objective Xin et al [13] 2011 Rule-based heuristic Dynamic multiobjective Fei et al [14] 2012 Auction algorithm Static single objective Bogdanowicz et al [15] 2013 GA Static single objective Liu et al [16] 2013 MOPSO Static multiobjective Zhang et al [17] 2014 MOEA/D Static multiobjective Ahner and Parson [18] 2015 Dynamic programming Dynamic multiobjective Li et al [19] 2015 NSGA-II, MOEA/D Static multiobjective Dirik et al [20] 2015 MILP Dynamic multiobjective Hongtao and Fengju [21] 2016 CSA Static single objective Li et al [22] 2016 MDE Dynamic multiobjective Li et al [23] 2017 MPACO Static multiobjective 2 International Journal of Aerospace Engineering population members and also allow the NSGA-III to perform well on MOP with differently scaled objective values. This is an advantage of the NSGA-III and another reason why we choose the NSGA-III algorithm to solve the SMWTA problem.…”

Section: Researchersmentioning

confidence: 99%

An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat

You

Kou

2018

International Journal of Aerospace Engineering

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Multiobjective weapon-target assignment is a type of NP-complete problem, and the reasonable assignment of weapons is beneficial to attack and defense. In order to simulate a real battlefield environment, we introduce a new objective-the value of fighter combat on the basis of the original two-objective model. The new three-objective model includes maximizing the expected damage of the enemy, minimizing the cost of missiles, and maximizing the value of fighter combat. To solve the problem with complex constraints, an improved nondominated sorting algorithm III is proposed in this paper. In the proposed algorithm, a series of reference points with good performances in convergence and distribution are continuously generated according to the current population to guide the evolution; otherwise, useless reference points are eliminated. Moreover, an online operator selection mechanism is incorporated into the NSGA-III framework to autonomously select the most suitable operator while solving the problem. Finally, the proposed algorithm is applied to a typical instance and compared with other algorithms to verify its feasibility and effectiveness. Simulation results show that the proposed algorithm is successfully applied to the multiobjective weapon-target assignment problem, which effectively improves the performance of the traditional NSGA-III and can produce better solutions than the two multiobjective optimization algorithms NSGA-II and MPACO.

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“…According to the model of asset-based MOSWTA problem, the individual of this problem has many constraints. Liang and Kang [23] designed a decimal encoding which can handle the constraints of this problem effectively. The illustration of this encoding is as follows.…”

Section: The Individual Encoding and Constraint Handlingmentioning

confidence: 99%

“…Xue et al [22] showed an improved discrete differential evolution (DE) algorithm for a cooperative jamming WTA problem. Hongtao and Fengju [23] proposed a new clonal selection algorithm for WTA problem. Chen et al [24] used memetic algorithms for DWTA.…”

Section: Introductionmentioning

confidence: 99%

Weapon-Target Assignment Problem by Multiobjective Evolutionary Algorithm Based on Decomposition

Zhou

Pan

et al. 2018

Complexity

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The weapon-target assignment (WTA) problem is a key issue in Command & Control (C2). Asset-based multiobjective static WTA (MOSWTA) problem is known as one of the notable issues of WTA. Since this is an NP-complete problem, multiobjective evolutionary algorithms (MOEAs) can be used to solve it effectively. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a practical and promising multiobjective optimization technique. However, MOEA/D is originally designed for continuous multiobjective optimization which loses its efficiency to discrete contexts. In this study, an improved MOEA/D is proposed to solve the asset-based MOSWTA problem. The defining characteristics of this problem are summarized and analyzed. According to these characteristics, an improved MOEA/D framework is introduced. A novel decomposition mechanism is designed. The mating restriction and selection operation are reformulated. Furthermore, a problem-specific population initialization method is presented to improve the efficiency of the proposed algorithm, and a novel nondominated solution-selection method is put forward to handle the constraints of Pareto front. Appropriate extensions of four MOEA variants are developed in comparison with the proposed algorithm on some generated scenarios. Extensive experiments demonstrate that the proposed method is effective and promising.

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Adaptive chaos parallel clonal selection algorithm for objective optimization in WTA application

Cited by 33 publications

References 14 publications

Designing a Framework for Target-Site Assignment in Naval Combat Management

Designing a Framework for Target-Site Assignment in Naval Combat Management

An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat

Weapon-Target Assignment Problem by Multiobjective Evolutionary Algorithm Based on Decomposition

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