关键词: 粒子群优化算法, 自适应简化粒子群算法, 群体智能, 基准函数, 无约束问题, 优化设计, 机床主轴

Abstract: In order to overcome the disadvantages of slow search speed, low convergence accuracy and high search volatility of particle swarm optimization, a Self-Adjusted Simplified Particle Swarm Optimization（SASPSO） algorithm has been proposed. During the evolutionary process, the particles are only affected by the global optima, and the locking factor distributed according to a certain rule is added so that the degree of influence of the particles can be traced. At the same time, the inertia weight is adaptively adjusted by using the locking factor and the current particle position, which makes the influence of inertia weight on the particle swarm optimization algorithm more effective. Four improved particle swarm algorithms proposed recently are introduced to simultaneously search for eighteen benchmark functions with different dimensions. The results are compared and T-test is used to analyze their differences. The success rates and the average iteration times are recorded when five improved algorithms search for 100-dimensional functions, which is beneficial for analyzing the performance of the proposed algorithm. Experimental results show that the convergence speed and optimization accuracy of SASPSO have been improved significantly, and the search results have lower objective function values and smaller standard deviations. The SASPSO is applied to the optimum design of machine-tool spindle parameters. The results show that the SASPSO optimization performance is better.

Key words: particle swarm optimization algorithm, self-adjusted simplified particle swarm optimization, swarm intelligence, benchmark function, unconstrained problem, optimum design, machine-tool spindle