Abstract
It is a crucial research to improve the efficiency of weapon-target assignment (WTA) of warship formation. However, the WTA problem is NP hard. Some heuristic intelligent algorithms usually result in local optimal. A novel genetic & ant colony optimization (GACO) algorithm is proposed which is based on the combination of genetic algorithm and ant colony algorithm. Genetic algorithm phase adopts crowding replacement and changeable mutation operator to create multiple populations. Due to the good initial pheromone distribution, ant colony optimization phase can avoid getting into local optimal. Then, a further study of how to use the algorithm on WTA is made. Some experiments are made. The results demonstrate that GACO has better efficiency than other classical algorithms. The bigger the WTA problem is concerned, the more advantage the algorithm makes. The proposed algorithm is viable for other NP-hard problems.
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