融合差分进化和Sine混沌的改进粒子群算法OA北大核心CSTPCD

Combining differential evolution with Sine chaos,an improved particle swarm optimization algorithm is pro-posed.It uses Sine chaotic mapping to optimize the initial population and improve convergence speed.This algorithm in-troduces a speed update formula for asynchronous learning factors and random inertia weights,enabling the algorithm to better balance global search and local optimization.It draws on the crossover operation in differential evolution algorithm,adopts a random search strategy with elimination mechanism to improve the algorithm's global search ability and conver-gence speed.To verify the performance of improved particle swarm optimization algorithm,compared with PSO related algorithms such as yield-based particle swarm optimization(YPSO),self-adaptive particle swarm optimization(SPSO),as well as the latest algorithms such as spider wasp optimization(SWO)and energy valley algorithm(EVA)in 2023,the ef-fectiveness of the improved particle swarm optimization algorithm(IPSO)that integrates differential evolution and Sine chaos is verified.It solves 12 commonly used benchmark functions in different dimensions,conducts experiments on 12 test functions,and compares them with other algorithms.The experimental results show that the improved PSO algorithm has fast convergence speed and high convergence accuracy.