关键词: 粒子群算法, 遗传算法, 交互学习, 机械动力学, 铣削参数优化

Abstract: There are many non-linear, multi-extremum and multidimensional complicated problems in the applications of the engineering field. This paper puts forward the Genetic Algorithm（GA） into the Particle Swarm Algorithm（PSO）, and uses the method of mutual learning to solve those problems. This method integrates the particle swarm algorithm’s simple theory and quick convergence with the genetic algorithm’s global search ability to get higher convergence precision, stronger execution and avoid falling into local optimal solution. The comparative analysis results show the parallel learning strategy has great advantages in terms of accuracy, efficiency and processing ability of different complexity problems. This algorithm is especially applicable to solve accurately and complex problems. Example shows that this algorithm can solve the nonlinear, multiple maximum and multi-dimension engineering problem existed in the milling parameters optimization solved by the machine dynamics theory.

Key words: Particle Swarm Optimization, Genetic Algorithm, mutual learning, machine dynamics, milling parameters optimization