Research On Hybrid Particle Swarm Optimization Algorithm Based On Cognitive Population

Particle Swarm Optimization?PSO?is a population adaptive group intelligence optimization algorithm proposed by Dr.Kennedy and Dr.Eberhart in 1995.The idea is derived from the foraging behavior of the birds,which is inspired by the process of flying the birds to the food.Since its inception,particle swarm optimization algorithm has been highly concerned by experts and scholars in various fields.Because of its simple principle,few adjustment parameters,and easy implementation,it has become a research hot spot of intelligent algorithm research scholars.Although PSO has practical applications in many fields,any algorithm itself is not perfect.PSO also has shortcomings such as slow convergence and easy to fall into local optimum.Therefore,in order to improve the performance of the algorithm and improve the defects of the particle swarm optimization algorithm itself,It is very important to improve the performance of the algorithm.The author attempts to improve the convergence and accuracy of the algorithm and improve the performance of the algorithm by mixing PSO with other intelligent algorithms.This article mainly completes the following aspects:1.This part introduce a basic overview of the particle swarm optimization algorithm,including the origin of the algorithm,the basic principles of the algorithm,the parameter settings of the algorithm,the standard particle swarm optimization algorithm,and finally the research of the hybrid particle swarm optimization algorithm.2.A particle swarm optimization algorithm based on population optimal distribution estimation?EPSO?is proposed.The estimation of distribution algorithm is mixed into the particle swarm optimization algorithm.The population update of each generation is found by the position update of the particle swarm optimization algorithm.The optimal population of each generation is used as the object of the"selection"of the estimation of distribution algorithm.Through the effective combination of the two,the numerical precision at the micro level can be guaranteed,and the macro convergence can be achieved quickly.Optimization and optimization of the optimization algorithm.3.Introducing the concept of cognitive population,the population consisting of the individual optimal values generated by each particle in the particle swarm optimization algorithm in each iteration is called the cognitive population of the particle swarm optimization algorithm{P₁?t?,P₂?t?,...,P_N?t?}.A hybrid study of population and distribution estimation algorithms is proposed to propose a population estimation based on cognitive populations?PPSO?.Tests on the convergence and function of the algorithm by ten test functions,experiments show that the improved algorithm improves the convergence and accuracy of the particle swarm optimization algorithm.4.In-depth study of the effect of cognitive population,the differential evolution algorithm and the set of mixed research,proposed a cognitive evolution based population evolution particle swarm optimization algorithm?DPSO?.Improve the performance of PSO by combining the variation,crossover,and selection operations of cognitive populations and differential evolution algorithms.Ten test functions are used to compare the convergence of the algorithm with EPSO and PPSO,the accuracy of the solution and the running time of the algorithm.The feasibility of the algorithm is verified by simulation experiments.