The Research And Improvement Of Swarm Intelligence Optimization Algorithm On Particle Swarm Optimization

Along with the continuous progress of society,there are more and more optimization problems in economic management, engineering practice, scientific research,etc,and the complexity and cumbersome degree have been represented a rapid growth trend.In the face of contemporary optimization problems' non-linear,large-scale,high real-time, low consumption features and so on,the traditional optimization method has been difficult to obtain good results,because in the beginning of the method design facing different application scenarios, different stages of hardware and software computer and the limitations of the method. Through a large number of scholars and experts' intensive study,swarm intelligence optimization algorithm has demonstrated its characteristic of high speed and high precision in solving contemporary optimization problems and play an important role in engineering practice.Particle Swarm Optimization(PSO) algorithm is a excellent swarm intelligence optimization algorithm,and the inspiration of PSO comes from simulating the foraging behavior of social gregarious organism to get optimum solution.The algorithm requires less condition when in searching,and the particle's basis movement in high-dimensional space getting from the fitness value.PSO is an adaptive optimization algorithm.As a kind of uncertain and probabilistic optimization algorithm,PSO algorithm has no strong restriction such as continuity and conductivity,and so on,and show its strong generality.The PSO algorithm has high search efficiency because it use distributed parallel search strategy in the process of optimization.Meanwhile,because of the limitation of PSO algorithm,further research and improvement is needed in theoretical research and practical application.This paper proposed more efficient algorithm for improvement and applied in engineering practice basing on the idea and mechanism of the PSO.In the research of PSO algorithm,this paper proposed a kind of shock search PSO algorithm based on kernal matrix synergistic evolution(KMSESPSO) due to shortcoming of particle swarm optimization(PSO) algorithm that it is often trapping in local optimum at the late stage based on the principle of PSO algorithm. The proposed algorithm do a combination of local and global shocks search and when the whole particle swarm is stagnant the evolution particle group would have a synergistic evolution to enrich the diversity of population by using kernel matrix.The experiment results show that the proposed algorithm strengthens the global search capability of particles effectively and can not only get free from premature but also raise the optimal accuracy in faster convergence speed and have certain robustness.This paper proposed a new method called Synergistic Shock particle swarm optimization algorithm based on Ransac With Chaos(RCSSPSO) by referencing the features and idea of KMSESPSO.Algorithm using the ergodic property of chaos to strengthen the local search ability of PSO. In order to enhance the ability of global optimization,RCSSPSO do some non-linear search in swarm based on the idea of Random Sample Consensus(Ransac).The algorithm do a non-linear synergistic evolution for the synergistic evolution set when the swarm run into serious stagnant to improve the accuracy of algorithm and the diversity of population. Resource-Constrained Project Scheduling Problem(RCPSP) generally exist in various industries in real life,all mission particles are always subject to time s equence constraints and resource constraints at the project execution stage,and the mission would be excuted only at the time when it meet these two constraints.The RCPSP has been proved to be NP-hard and the study of RCPSP has important significance for t he prediction and adjustment of the project process. Due to the RCPSP, the gotten scheduling scheme in this paper is based on the particle's topological sort and serial scheduling generation scheme. To further verify the effectiveness of the algorithm,this paper tests the algorithm on specific scheduling projects.The result shows that the algorithm gets better accuracy and robustness when solving RCPSP because of shorter project cycle and lower average deviation rate of the shortest project period and have generally theoretical meaning and engineering practicing value.