Swarm intelligence
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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In this paper, based on the analysis of existent evaluation methods for flatness errors, an intelligent evaluation method is provided. The evolutional optimum model and the calculation process are introduced in detail. According to characteristics of flatness error evaluation, Particle Swarm Optimization (PSO) is proposed to evaluate the minimum zone error. Compared with conventional optimum methods such as simplex search and Powell method, it can find the global optimal solution, and the precision of calculating result is very good. Then, the objective function calculation approaches for using the PSO to evaluate minimum zone error are formulated. Finally, the control experiment results evaluated by different method such as the least square, simplex search, Powell optimum methods and GA, indicate that the proposed method does provide better accuracy on flatness error evaluation, and it has fast convergent speed as well as using computer easily and popularizing application easily.