The Research On The Optimization And Design Of Multi-agent Systems With Cooperation

With the advancement of the social economy and the development of computer technology,the promotion and improvement of artificial intelligence technology are changing rapidly.Driven by the high-performance computing and artificial intelligence algorithms,multi-agent systems(MAS),as one of the main branches of distributed artificial intelligence,have been widely used in machine learning,social networks,financial networks,economic evolution,and game theory,etc.,including but not limited to computer science,biology,medicine,economics,and psychology.MAS has become the first choice for solving tasks in a large-scale complex system and the related simulation and analysis.The self-interest,sociality,responsiveness,pro-activeness,and efficient problem-solving of agents in MAS have provided a new Perspective for solving practical application problems such as social network applications,engineering scheduling,path planning,and obstacle avoidance.According to different application fields and practical problems,MAS design has considerable flexibility which makes MAS has a variety of types.However,no matter what the structure of the MAS type is,the cooperation between agents plays a significant role in the process of problem-solving and achieving the goal of MAS.The execution efficiency and related performance of MAS in a variety of organization formations(such as allocation,team,and congregation)depend on the design of the agent cooperation mechanism.Besides,there are many constraints and application rules when dealing with different large-scale and complex problems,which brings new challenges and requirements to the model design and cooperation optimization of MAS.Therefore,in order to improve the efficiency,performance and scalability of MAS in solving related practical problems,this paper conducts in-depth research on the design and optimization of multi-agent systems based on cooperation.In this dissertation,the key techniques of uncertain skyline query is researched,and the main jobs and innovations are as follows:(1)Aiming at the constantly changing connections in complex networks,a dynamic coalition cooperation mechanism is designed for the cooperation of multi-agents,which makes up for the shortcomings of existing technologies that cannot deal with the cooperation of multiagents in a dynamic environment.In a dynamic complex network,this cooperation mechanism is based on the background of agent resource exchanging,and improves the dynamic cooperation in the resource buying and selling game among the agent.The core idea of this dynamic mechanism is the idea of ”stronger” agents,which can dynamically calculate and update the core-agent.The agent will dynamically change its coalition according to the changes of the core-agents and the evaluations of its own environment in each round of the game iteration.In this way,the final MAS's social cooperation benefit reaches the highest level.(2)According to the problem of multi-task allocation and optimization in a multi-agent system in a cooperative environment,a quantum PSO algorithm to achieve stable cooperation and efficient execution.The purpose of multi-task assignment in multi-agent systems is to complete tasks with high efficiency and a high success rate and at the same time obtain corresponding task rewards.Then it can promote the efficient operation of the entire system.The defects of most existing task allocation methods are mainly concentrated in 1)there is no balance between the reward distribution of the agent and the success rate of task execution;2)ignoring of the stability of the coalition cooperation for completing the task.Such defects can easily lead to a low task execution rate and task assignment failure.Furthermore,the task allocation method that lacks coalition scheduling often leads to a long makespan and causes conflicts between the agents.An efficient quantum particle swarm optimization algorithm(SQPSO)based on coalition stability is proposed,which ensures task allocation on the aspect of the agent's profits,reward dividing,and coalition stability for task execution,as well as the speed of searching for the best coalition.Based on the allocation results calculated by SQPSO,an effective coalition scheduling algorithm(EQPSO)is designed.Its unique coalition similarity judgment can help formulate the best scheduling strategy to reduce the makespan.The two-step calculation based on SQPSO and EQPSO algorithms optimize the process and solution of the multi-task allocation problem in MAS.(3)Considering task dividing and multi-objective optimal allocation under the task allocation problem in a cooperative multi-agent system,a novel hierarchical MAS model is constructed.The task dividing and allocation are solved and optimized through deep Q-learning and many-objective optimization methods.The hierarchical MAS model provides a basis for task division and reorganization and multi-objective task assignment.In the first layer,a deep Q-learning dividing algorithm is proposed to simplify the complex prioritization of the original task set through the process of task dividing and regrouping.In the second layer,a modified shift-based density estimation method(MSDE)is proposed for the population screening process of the MSDE-SPEA2-based algorithm.The MSDE-SPEA2-based algorithm accomplishes the multi-objective optimization of task allocation,including five goals: makespan,agent satisfaction,resource utilization,task completion and task waiting time,and it also solves the task allocation and scheduling simultaneously.(4)According to the optimization problem of influence propagation of multi-agent systems under unstable links,from the perspective of agent cooperation and interaction,the model of influence maximization(IM)problem is designed on MAS.Combined with graph embedding method and deep learning idea,the IM problem under the unstable links has been solved.Firstly,the definition and concept of unstable connection are proposed,and the definition of IM in MAS under unstable links is described.Secondly,based on the definition of the problem,the IM model of MAS is designed,including the availability of unstable connections and two new diffusion models.Then based on the problem and model,the agent interaction algorithm is designed,which includes the agents' interaction rules and information communications.Finally,the Unstable-Similarity2vec(US2vec)algorithm is proposed to embed the nodes with their information under unstable links.Then the selection algorithm(CA)is proposed to decide the seed set,which accomplishes the optimization of IM under the unstable links in MAS.