Weapon-Target Assignment Problem in the Warship Fleet Based on Fast and Elitist Non-Dominated Sorting Genetic Algorithm

2012 ◽  
Vol 605-607 ◽  
pp. 2399-2404
Author(s):  
Xin Lai Chen ◽  
Song Shen
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Optimization Problem ◽  
Pareto Solution ◽  
Robust Solution ◽  
Target Assignment

Represent a new Weapon-Target Assignment (WTA) model of warship fleet as to the characteristic of the modern naval battle field and the battle modality. This model considers the WTA to a multi-objects optimization problem, and a Fast and Elitist Non-Dominated Sorting Genetic Algorithm (FENSGA) is applied to resolve this model. The FENSGA can reach a set of wide-distributing, robust solution. One running of the FENSGA can reach a multi-Pareto solution, which the commander can select from. A simulation is given to prove the validity of this model and algorithm.

An Application of Quantum-inspired Genetic Algorithm for Weapon Target Assignment Problem

2017 ◽  
Vol 40 (4) ◽  
pp. 260-267 ◽  
Author(s):  
Jung Hun Kim ◽  
◽  
Kyeongtaek Kim ◽  
Bong-Wan Choi ◽  
Jae Joon Suh
Keyword(s):  
Genetic Algorithm ◽  
Assignment Problem ◽  
Target Assignment

Integrated Optimization of ACLD Treatment for Shape Control of Flexible Beams Using Multi-Objective Genetic Algorithm

Aerospace ◽  
2003 ◽  
Author(s):  
L. C. Hau ◽  
Eric H. K. Fung
Keyword(s):  
Genetic Algorithm ◽  
Shape Control ◽  
Optimization Problem ◽  
Viscoelastic Layer ◽  
Flexible Beams ◽  
Pareto Solution ◽  
Integrated Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

This paper presents the use of multi-objective genetic algorithm (MOGA) to solve an integrated optimization problem for the shape control of flexible beams with Active Constrained Layer Damping (ACLD) treatment. The design objectives are to minimize the total weight of the system, the input voltage and the steady-state error between the achieved and desired shapes. Design variables include the thickness of the constraining layer and viscoelastic layer, the length and location of the ACLD patches, as well as the control gains. In order to evaluate the effect of different combinations of design variables on the system performance, the finite element method, in conjunction with the Golla-Hughes-McTavish (GHM) method, is employed to model a clamped-free beam with ACLD patches. As a result of the optimization, a Pareto solution is successfully obtained. It is shown that the MOGA is applicable to the present integrated optimization problem, and ACLD treatment is suitable for shape control of flexible structures.

scholarly journals A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem

2019 ◽  
Vol 9 (18) ◽  
pp. 3803 ◽  
Author(s):  
Xiaoyang Li ◽  
Deyun Zhou ◽  
Zhen Yang ◽  
Qian Pan ◽  
Jichuan Huang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Problem ◽  
State Of The Art ◽  
Experimental Studies ◽  
Combinatorial Optimization Problem ◽  
Specific Population ◽  
Operational Effectiveness ◽  
Model Based ◽  
Target Assignment

The sensor-weapon–target assignment (S-WTA) problem is a crucial decision issue in C4ISR. The cooperative engagement capability (CEC) of sensors and weapons depends on the S-WTA schemes, which can greatly affect the operational effectiveness. In this paper, a mathematical model based on the synthetical framework of the S-WTA problem is established, combining the dependent and independent cooperative engagement modes of sensors and weapons. As this problem is a complex combinatorial optimization problem, a novel genetic algorithm is proposed to improve the solution of this formulated S-WTA model. Based on the prior knowledge of this problem, a problem-specific population initialization method and two novel repair operators are introduced. The performances of the proposed algorithm have been validated on the known benchmarks. Extensive experimental studies compared with three state-of-the-art approaches demonstrate that the proposed algorithm can generate better assignment schemes for the most of the benchmarks.

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