Applications Of A Co-evolutionary Particle Swarm Optimization Algorithm In Software Multi-project Scheduling Problems

Software multi-project scheduling is a kind of complex combinatorial optimization problem.There is no such an algorithm that is particularly ideal for solving the convergence problem of such practical application problems,and the mathematical model for solving such problems usually has a certain gap with the actual demand.In order to better solve the human resource conflict problem in software multi-project scheduling problem,how to design an evolutionary algorithm that can minimize the project duration becomes the key research direction in the field of evolutionary computing.Based on the above background,the research content of this paper is as follows:1)Aiming at the shortcomings of standard PSO algorithm,such as local convergence and low precision,an improved particle swarm optimization algorithm based on Levy flight is proposed.The inertia weight is adaptively adjusted according to the current state of the particle.As the number of iterations increases,the learning factor Non-linear monotonically decreasing,and designing a new speed update strategy to achieve a reasonable balance between global exploration and local development.When the particles are aggregated to a certain extent,the mutation update strategy is adopted for the particle position with a certain probability,and the improved particle swarm optimization algorithm based on Levy flight is compared with several existing improved particle swarm optimization algorithms.The experimental results show that the improved particle swarm optimization algorithm based on Levy flight is obvious in the optimization results and convergence speed.Better than other improved algorithms.2)Aiming at the shortcomings of the traditional optimization algorithm with the increase of the dimension and the sharp decline of the performance of the algorithm,an improved co-evolutionary particle swarm optimization algorithm is proposed.Under the framework of co-evolution,a new sub-population contribution formula is proposed.Based on the contribution degree,the computational resources are rationally allocated to the sub-population.The improved particle swarm optimization algorithm is used as thesub-population optimizer and the advanced sub-group cooperation mode is utilized to jointly constitute the improved coevolutionary particle swarm algorithm.The proposed improvement mechanism is compared with the existing co-evolution mechanism.The experimental results show that the proposed sub-population resource allocation method has better search advantages than the existing co-evolution framework.3)For the practical application of software multi-project scheduling,this paper establishes a scheduling mathematical model that satisfies the real needs and constraints.The project duration is minimized as the optimization goal,and the proposed model was simulated by the improved co-evolutionary particle swarm optimization algorithm.The simulation is solved and compared with the existing co-evolutionary algorithm.The experimental results show that the improved co-evolutionary particle swarm optimization algorithm can solve the software multi-project scheduling problem more effectively with faster convergence speed.