Target assignment in cooperative attacking of UCAVs based on multi-intelligence improved glowworm swarm optimization algorithm

A target allocation algorithm based on multi-intelligence improved glowworm swarm optimization (MIGSO) algorithm is proposed. A model of decision-making is built up by taking benefit index, loss index and range index as the criteria, and the MIGSO is used to solve the model. Finally the optimal allocation scheme for multi-aircraft cooperative attacking is gotten. According to the characteristics of UCAV attack decision making, a special coding for firefly particle and firefly update strategy is presented. With shuffled frog leaping algorithm (SFLA), glowworms are divided into different ethnic groups, and local search and global information exchange method improves GSO performance. SFLA is also combined with GSO, which realize the co-evolution of the two kinds of groups. The simulation results shows that the MIGSO algorithm can give the optimal target assignment solution quickly and effectively.