改进分解进化算法求解动态火力分配多目标优化模型

战前制定合理的火力分配(WTA)方案,可以优化资源配置,用最小的代价获取最大的战场收益。其一,建立了面向多型武器协同进攻作战的动态火力分配(DWTA)多目标优化模型,由多个阶段静态模型构成,各阶段静态模型参数需根据战场态势实时获取;其二,重点研究阶段静态模型求解算法。针对模型特点,设计了一种满足资源约束的编码方式,融合禁忌搜索和拥挤距离策略,提出了一种改进分解进化算法。对比实验验证了算法的可行性、快速性和有效性。

Improved Decomposition-Based Evolutionary Algorithm for Multi-objective Optimization Model of Dynamic Weapon-target Assignment

A reasonable weapon-target assignment(WTA) scheme is developed to optimize the allocation of limited resources, which brings the maximum awards with minimum costs. A dynamic weapon-target assignment(DWTA) multi-objective optimization model is established, especially for the offensive operation with multi-weapon. The proposed model is composed of several stage static models. The parameters of each stage model are obtained from the battlefield in real time. An algorithm is elaborately studied to solve the stage model based on the hypothesis to deal with the dynamic model. According to the characteristic of the proposed model, an encoding mode is constructed to satisfy the resource constraints. An improved decomposition-based evolutionary algorithm is proposed by mixing of tabu search and crowding distance strategies. Comparative experiments prove that the proposed algorithm is feasible, fast and efficient.