The weapon target assignment (WTA) problem, which has received much attention in the literature and is of continuing relevance, seeks within an air defense context to assign interceptors (weapons) to incoming missiles (targets) to maximize the probability of destroying the missiles. Kline et al. (J Heuristics 25:1–21, 2018) developed a heuristic algorithm based upon the solution to the Quiz Problem to solve the WTA. This heuristic found solutions within 6% of optimal, on average, for smaller problem instances and, when compared to a leading WTA heuristic from the literature, identified superlative solutions for larger instances within hundredths of a second, in lieu of minutes or hours of computational effort. Herein, we propose and test an improvement to the aforementioned heuristic, wherein a modified implementation iteratively blocks exiting assignments to an initial feasible solution, allowing superior solutions that would otherwise be prevented via a greedy selection process to be found. We compare these results to the optimal solutions as reported by a leading global optimization solver (i.e., BARON) and find solutions that are, at worst, within 2% of optimality and, at best, up to 64% better than the solutions reported to be optimal by BARON. To wit, the developed metaheuristic outperformed BARON in 25% of all instances tested, as BARON reported a suboptimal solution as being optimal for 21.1% of the instances, and it could not identify an optimal solution for the remaining 6.67% of the instances within 2 h of CPU time, a liberally imposed time limit that far exceeds practical usage considerations for this application.

武器目标分配（WTA）问题已在文献中引起了广泛关注，并且具有持续的相关性，它试图在防空环境中为拦截的导弹（目标）分配拦截器（武器），以最大程度地破坏导弹。克莱恩等。（J Heuristics 25：1-21，2018）基于测验问题的解决方案开发了一种启发式算法来解决WTA。这种启发式方法发现较小问题实例的平均解决方案平均在6％的范围内，与文献中领先的WTA启发式方法相比，该算法在几分之一秒内（而不是几分钟或几小时的计算）为大型实例确定了最高级的解决方案。努力。在此，我们提出并测试了对上述启发式算法的改进，其中，修改的实施方式迭代地阻止对初始可行解决方案的退出分配，从而允许通过贪婪的选择过程来找到本来可以避免的高级解决方案。我们将这些结果与领先的全球优化求解器（即BARON）报告的最优解决方案进行比较，发现最差的解决方案比最优解决方案少2％，最好的情况下要比报告的解决方案好64％由BARON优化。综上所述，已开发的元启发法在所有测试实例中的25％均胜过BARON，因为BARON报告的次优解决方案对于21.1％的实例而言是最优的，并且无法确定在2个实例中其余6.67％的实例的最优解。 CPU时间h，一个自由施加的时间限制，远远超出了此应用程序的实际使用注意事项。