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Genetic algorithm with simulated annealing for laser antimissile optimization

机译:模拟退火的遗传算法用于激光反导优化

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A dynamical laser antimissile problem is solved based on genetic algorithm with simulated annealing considering the real time application. When faced with multiple attacking targets, it is clearly important for the laser antimissile system to determine the sequence of the attacking targets to be intercepted so that the maximum attacking targets are destroyed. Conceptually, this laser antimissile problem can be seen as a dynamical traveling salesman problem, which is much harder than a pure traveling salesman problem. There are many barriers for finding the global optimal solution, especially when the number of the targets is greater than six. The genetic algorithm has the capability of searching in wider space. While the simulated annealing algorithm can jump from the local optimal solution. Therefore, the combination of the genetic algorithm and the simulated annealing algorithm has their merits. Taking ten targets as an example, the simulation results show that the proposed algorithm has the better property and can be used in the real application.
机译:考虑到实时应用,基于遗传算法的模拟退火解决了动态激光反导问题。当面对多个攻击目标时,激光反导系统确定要拦截的攻击目标的顺序,以使最大的攻击目标被破坏,显然很重要。从概念上讲,此激光反导问题可以看作是动态的旅行推销员问题,它比单纯的旅行推销员问题难得多。寻找全局最优解有很多障碍,尤其是当目标数量大于六个时。遗传算法具有在更广阔的空间中搜索的能力。而模拟退火算法可以从局部最优解中跳出来。因此,遗传算法和模拟退火算法的结合具有其优点。仿真结果以十个目标为例,表明该算法具有较好的性能,可在实际应用中使用。

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