Title
Constrained Multi-Objective Weapon Target Assignment for Area Targets by Efficient Evolutionary Algorithm
Abstract
The weapon target assignment (WTA) problem is the crucial decision support in Command & Control (C2). In the classic WTA model, the point-to-point saturation salvo has a low efficiency-cost ratio when the swarming targets, which have the advantage of low casualty, low cost and recyclable, become the major operational units. The constraint is less studied for the operational intention of the decision-maker. In this paper, a constrained multi-objective weapon target assignment (CMWTA) model is formulated for area targets. The optimization objectives are minimizing collateral damage and resource consumption. The multi-constraint is derived from the actual operational requirements of security evasion, damage threshold, and preference assignment. To solve CMWTA efficiently, a novel multi-objective optimization evolutionary algorithm (MOEA) is proposed to obtain the non-dominated solutions as the alternative plans for the decision-maker. A self-adaptive sorting algorithm is proposed to guarantee the completeness of the Pareto-optimal set, and a cooperative evolutionary mechanism is adopted to strengthen the convergence. For handling multi-constraint, a repair mechanism is proposed to improve the quality of infeasible solutions, and the measurement of constraint violation is designed to evaluate the infeasible solutions. A variant of the convergence metric is introduced to evaluate the algorithms solving multi-objective weapon target assignment (MWTA) problem. The experimental results show the effectiveness and superiority of the proposed approaches.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2955482
IEEE ACCESS
Keywords
DocType
Volume
Constrained weapon target assignment,collateral damage,multi-objective optimization algorithm,decision support system
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Kai Zhang100.68
Deyun Zhou25610.70
Zhen Yang331.88
Qian Pan433.84
Weiren Kong510.69