Research Of Swarm Intelligence Algorithm Based On Particle Swarm And Bird Swarm Optimization

In scientific computing and engineering applications,the optimization problem is a very important research subject.As a highly efficient parallel heuristic search algorithm,particle swarm and bird swarm optimization algorithms in solving optimization problems are showed the superiority of its own,but there are still defects.In this paper,We studied the problems of slow convergence speed and low optimization accuracy of particle swarm and bird swarm optimization algorithms in order to make up for the shortcomings of the two algorithms.The main research contents of this paper are as follows:1.We summarized the particle swarm and bird swarm optimization algorithms,and alao analyzed the advantages and disadvantages of the two algorithms.Meanwhile we proposed two improved strategies for the particle swarm and bird swarm optimization algorithms respectively.2.In view of the problem that the particle swarm optimization algorithm has premature convergence and it is easy to fall into local optimum,we proposed two strategies to improve.The parameters in the particle swarm optimization algorithm are adjusted adaptively to balance the local search and global search capability of the algorithm.By introducing chaotic dynamic weights,we improved the updating formula of particle swarm optimization algorithm and accelerated the convergence performance of the algorithm.By establishing a robot path planning model,so that we improved particle swarm optimization algorithm to apply to solve the robot path planning problem.And the effectiveness of the algorithm is verified by numerical experiments.3.Aiming at the problems that the bird swarm optimization algorithm has slow convergence rate and low optimization accuracy in solving high-dimensional complex optimization problems,we also proposed two improved strategies.The inertia weight is introduced into the bird swarm optimization algorithm to modify the foraging strategy and enhance the diversity of the algorithm.By introducing the cloud theory and the concept of mean value,we improved for updating formula of the optimization algorithm,and accelerated the search performance of its algorithm.By means of establishing an optimization model of agricultural products cold chain logistics distribution path,we will improve bird swarm optimization algorithm is used to solve the optimization problem of cold chain logistics distribution path of agricultural products.Finally,the feasibility of the algorithm is verified by numerical test.4.In order to make up for the shortage of local search ability and uniformity of the multi-objective particle swarm optimization algorithm,we introduced a competition mechanism strategy is used to quickly search for non-dominant solutions and dynamically adjust the parameters of particles in the multi-objective particle swarm optimization algorithm.And introduced time-varying gaussian variation in the later stage of the algorithm.Numerical experiments show that the algorithm in convergence and diversity are improved.5.In this paper,in order to applied the bird swarm algorithm to solve the multi-objective optimization problem,we proposed a multi-objective bird swarm optimization algorithm,and introduced the clone immunity strategy to enhance the randomness and diversity of the algorithm.Numerical experiments show that the multi-objective bird swarm optimization algorithm has good convergence and diversity.