An optimal allocation approach of cooperative search capability based on game theory

Abstract

Since the search theory was put forward in the period of WWII, the search problem has become a research hotspot. As the search tasks are getting more varied, cooperative search is becoming a trend. By analysis on the “Two-way Search” problem in multiple UAVs cooperative search operations, the searchers and the targets were considered to form a game. As the searchers allocate their members to detect the target, and the target moves in a cell to evade the searchers, this type of the search game is called a search allocation game (SAG). Therefore, a SAG model was built to solve the SAG problem. However, the amount of the searchers' strategies is so big that we can't find an effective way to obtain an optimal strategy. Then, an optimizational SAG model was built to reduce the amount of the strategies. In order to obtain an optimal strategy, particle swarm optimization (PSO) was applied into solving this problem. In the end, a computational experiment was carried out to prove the effectiveness of the methodology proposed. The computing results showed that the optimizational model can reduce the amount of the searchers' strategies greatly. Besides, PSO could obtain the optimal mixed strategy as well compared with the function “linprog” in matlab, what's more, PSO had a better computing rate.