Research On A Distributed Platform For Evolutionary Computation Based On Container And Micro-service

With the advent of big data,the amount of newly generated data has grown exponentially,and the scale of optimization problems that people face has also increased.The evolutionary computation methods that are commonly used to solve such problems are not satisfactory under large-scale data.In the traditional development mode of distributed computing and cloud platforms,it is necessary to develop targeted computing applications for each optimization problem,wasting a lot of resources.The distributed heuristic algorithm platform that appeared later usually has deficiencies such as complex deployment,low degree of parallelization,difficulty in scaling,unfriendly user and developer interfaces,and insufficient support for algorithms and problems.This thesis aims to overcome the shortcomings of the above computing platform and provide a distributed computing platform with easy deployment,flexible expansion,friendly interface,and wide coverage for evolutionary computing.This thesis explores the feasibility of distributed applications of evolutionary algorithms,analyzes the architecture and functional requirements of the platform,selects technologies based on these requirements,and designs the architecture of the entire platform.This article uses container technology and microservice architecture to divide the platform functions,and design and implement resource management microservices,task control microservices,and RESTful user interfaces.In this thesis an algorithm library is designed and implemented in conjunction with a typical distributed evolutionary calculation model,and the development specifications of the problem library are defined,which provides developers with a convenient way to expand algorithms and problems.The implementation method of the entire architecture of the evolutionary computing platform is introduced in detail,and each component in the platform is explained.In addition,this paper also selects three typical complex optimization problems,uses these problems to explain the implementation principle and method of the combinatorial optimization problem on the distributed evolutionary computing platform,and tests the design function of the platform.