Research And Application Of Particle Swarm Optimization And Job Shop Scheduling Problem Based On Compound Chain P System

Membrane system is a new parallel computing model inspired by biological cells,tissues,organs,etc.,and its proposal has caused a large number of scholars to study.Its biggest feature is that it has distributed and great parallelism.It can replace the complexity of time with the complexity of space,thus greatly reducing the running time of the algorithm.Many researchers have tried to implement or improve various programming algorithms with membrane systems,and use the unique advantages of membrane systems to reduce the time complexity of the algorithms in order to obtain faster operating speeds.Particle swarm optimization is one of the intelligent algorithms.It is an intelligent optimization algorithm inspired by the state of the bird swarm when it flies.Because the algorithm is relatively simple and its convergence speed is fast,the particle swarm algorithm quickly became hot after it was proposed Topics are widely used by researchers at home and abroad to solve practical applications,and their applications cover many fields such as artificial intelligence,computer science,and statistical planning.However,since the particle swarm optimization algorithm can easily fall into a local optimum during the running process,many domestic and foreign researchers are now studying the optimization problem of the particle swarm optimization algorithm.Aiming at the shortcomings of particle swarm optimization,this thesis embeds an improved genetic algorithm and combines it with a membrane system to propose a new operation algorithm.Job shop scheduling problem is a process that solves the problem of combination optimization.Under the various constraints faced by enterprises,reasonable planning and distribution of various processes in the workshop production are sought to find a production with less production time or lower cost consumption.Process to achieve the goal of improving production efficiency.Workshop scheduling is a recognized NP-Hard problem,because it involves more variables and constraints,making its solution more complicated.This thesis applies the proposed new algorithm to job shop scheduling problems of different sizes,and the results prove that the algorithm can effectively solve JSSP.The main innovations of this article are as follows:(1)A composite chain organization P system(CTP)is designed.Firstly,the organization structure of forward and reverse single strands was proposed.All cells were unidirectionally connected into a chain,and the cells on the chain could communicate in a single forward direction and a single reverse direction.Secondly,the properties and functions of the cell-like P system and the tissue-like P system are combined,so that each cell in the tissue has the properties of an active membrane,and has derivatization and solubility.Finally,the proposed new organizational structure is used in the combined system,and the CTP system is designed.In the new system,cell activities are more flexible and information exchange is more convenient.(2)Applying the clone selection strategy to the selection operation of genetic algorithms,combined with the elite selection strategy,retaining the best-fit individuals,replacing low-matching antibodies with highly-matching clones according to a certain proportion,and then performing roulette Select to select the feasible solution set.With this method,a high-quality feasible solution can be selected,and the convergence speed of the algorithm is shortened.At the same time,in the cross operation,a threshold is set to compare it with the coincidence degree,and whether to perform the cross operation is determined according to the comparison result.This threshold constraint improves the diversity of understanding and reduces the probability of the algorithm falling into a local optimum.(3)In the PSO algorithm,the inertia weight is first improved based on the shrinkage factor,and then combined with the improved genetic algorithm to balance the global and local search capabilities of the particles and improve the accuracy of the algorithm.At the same time,the proposed composite chain organization P system is combined with the improved PSO algorithm,and the great parallelism and distributed characteristics of the P system are used to greatly improve the algorithm's operating speed.