Evolutionary Multi-objective Decision-making Methods And Their Application In Virtual Air Combat Simulation System

In recent years, along with the development of the integration, automation and intelligentization of the aerial warfare weapon system ,as the multi-fighters cooperative combat beyond visual range has been the primary form of modern air combat, the study on it is now showing more important than ever.In air combat simulation system of decision-making, through the advantage assessment and threat assessment, to choose the program for overall- largest-advantages and the overall-fewest-threats as the target allocation program when all our friends-fighter facing to enemy-fighter, belongs to the multi-objective optimization problem in essence.In this paper, we discus how to distribute the firepower for the multi-objective optimization, provideing a particle-swarm -multi-objective-optimization-algorithm, which is based on the strategy game theory ,and apply it to the problem of how to distribute the firepower, establishing an virtual air combat simulation prototype system.. The main work is as follows:First of all, we analysis the advantages assessment and threat assessment which is the core of virtual air combat decision-making and offer the model of the air firepower distribution of multi-objective optimization problemThen, we reform the algorithm which is based on the multi-objective particle-swarm-optimization, offering an algorithm on the base of the distribution of particle swarm optimization and take advantage of the balance between the objectives of the search of game theory to close the Pareto frontier solution to provide the good-distribution-solution -programs for the policy-makers . The method is better than the traditional multi-objective-optimization-algorithm, it can get better distribution of the candidate solution space for policy-makersFinally, we apply the algorithm proposed in this article into the virtual-air-combat-decision-making-simulation-prototype-system. The expected results of simulation-prototype-system in multi-objective-allocation of the virtual air combat, indicates the feasibility of the algorithm that based on particle swarm optimization algorithm, and the improved algorithm will be superior to the standard traditional particle swarm algorithm in the way of convergence speed and the solution- stability, as well as the solution -distribution. Multi-objective optimization algorithm is based on the multi-objective decision-making, in this paper, for the distribution of air power multi-objective optimization problem, we provide an efficient multi-objective algorithm which is based on multi-objective particle -swarm optimization, and apply it in air combat decision-making. The application effect and experimental results has shown that the method has the better nature of the constringency and the distribution of the solution-set , so this article is certainly meaningful in its application value.