Constrained Multi-Objective Weapon Target Assignment for Area Targets by Efficient Evolutionary Algorithm

Kai Zhang, Deyun Zhou, Zhen Yang, Qian Pan, Weiren Kong

科研成果: 期刊稿件文章同行评审

24 引用 (Scopus)

摘要

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.

源语言英语
文章编号8911381
页(从-至)176339-176360
页数22
期刊IEEE Access
7
DOI
出版状态已出版 - 2019

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