Research On The Application Of Improved Particle Swarm Optimization In Resources Constrained Project Scheduling Problem

The method for solving resource constrained project scheduling problem (RCPSP) has been researched as a priority area by domestic and foreign scholars, because it not only has the theoretical significance, but also has practical application value. PSO (Particle Swarm Optimization, PSO) is an important algorithm for solving the RCPSP. This paper aimed to the characteristics of the RCPSP, research and improve the PSO for solving RCPSP to improve the performance of the algorithm.There is a comparative study on genetic algorithms, ant colony algorithm and PSO in this paper. For the advantages and disadvantages of PSO, the paper proposes from both a combination of PSO and other algorithms and PSO parameter optimization to improve algorithm.Paper firstly according to the basic principles of PSO, and processes combining DE with PSO to form a hybrid algorithm-Differential Evolution Particle Swarm Optimization (DEPSO). Which uses two-population to respectively iterate, mechanisms for information exchange in the two populations is established, so that the two populations can share information in the iterative process of algorithm, to effectively reduce the risk of PSO into a local optimum. Using the PROJECT SCHEDULING PROBLEM LIBRARY-PSPLIB to test the algorithm, verified the performance of the hybrid algorithm.To further improve the algorithm's global search and local search capabilities and accuracy, paper introduces the cloud model to improve the inertia weight of PSO in hybrid algorithm; to make particles in PSO population can automatically adjust the inertia weight value in iterative process. The improved algorithm was tested by standard test functions and PROJECT SCHEDULING PROBLEM LIBRARY-PSPLIB, verified the effectiveness of the algorithm.Finally, the Differential Evolution Particle Swarm Optimization based on cloud model is used to solve the X Enterprise motorcycle new product development project scheduling problem in this paper, and indicated that the algorithm has a practical application value.