Minimum zone sphericity evaluation based on a modified cuckoo search algorithm with fuzzy logic

This study proposes an intelligent algorithm that combines a modified cuckoo search (MCS) algorithm with an adaptive fuzzy logic controller (MCSF) to evaluate minimum zone sphericity with high speed and accuracy. To accelerate the convergence of the iteration procedure, the proposed MCS algorithm introduces an optimized initial solutions distribution model and a modified biased/selective mechanism to the original cuckoo search. Moreover, an adaptive fuzzy logic controller is designed to ensure global search ability in the early stage of the search process and strong convergence performance in the later stage of iteration. This is achieved by adaptively adjusting the control coefficient a of step size and the probability p(a) of discovering an invasive cuckoo's egg in MCS. Simulation and comparison results indicate that the proposed method achieves excellent convergence behavior and reaches the global optima faster than conventional heuristic methods. The MCSF method runs 60 iterations in similar to 0.4 s, and its results for the standard deviation of sphericity are 1/8-1/2 of those of the conventional heuristic methods. The proposed method can easily be extended to evaluate minimum zone roundness, cylindricity, and straightness.