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首页> 外文期刊>International journal of aerospace engineering >An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat
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An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat

机译：一种解决多目标武器目标分配问题的改进非控制排序遗传算法III方法：第一部分：战斗力的价值

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摘要

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.
机译：多目标武器目标分配是NP完全问题的一种，合理分配武器有利于攻防。为了模拟真实的战场环境，我们引入了一个新的目标-在原始的两目标模型的基础上，战斗机的作战价值。新的三目标模型包括最大化敌人的预期伤害，最小化导弹成本以及最大化战斗机作战价值。为解决约束复杂的问题，提出了一种改进的非支配排序算法III。该算法根据当前种群不断生成一系列收敛性和分布性好的参考点，以指导进化。否则，将删除无用的参考点。此外，NSGA-III框架中集成了在线操作员选择机制，可以在解决问题的同时自主选择最合适的操作员。最后，将该算法应用于典型实例，并与其他算法进行比较，验证了该算法的可行性和有效性。仿真结果表明，该算法已成功应用于多目标武器—目标分配问题，有效地提高了传统NSGA-III的性能，并且比两种多目标优化算法NSGA-II和MPACO能够提供更好的解决方案。

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