A hybrid strategy based on multi-agent PSO for arms Optimal apportionment of regional air-defense

Abstract

Arms apportionment programming is a NP-hard problem. Detailed mathematical models for regional air-defense arms optimal apportionment are established. A novel algorithm named MAHOS (multi-agent hybrid optimization strategy) is proposed in order to solve this problem efficiently. The MAHOS introduces competition-cooperation, self-learning and simulated annealing mechanism into behaviors of particle agents, which improve the convergence rate and optimization precision of the algorithm. Simulation experiments of the problem are made at different scales. The results show that MAHOS is very efficient and effective in obtaining near optimal solutions to the air-defense arms optimal apportionment problems, especially when the scale of problems is very large. The MAHOS can offer a scientific and effective support for a decision maker in command automation of the air-defense combat.