(untitled) (Journal of Academic Research, 2019)
In this work, an attempt has been taken to display a brief idea about the applications of Nature-inspired optimisation algorithms in automated manufacturing systems. A new technigue based on the use of the Cuckoo Search optomization algorithm for flatness error estimation is proposed. The proposed technique has been validated and compared with will known optimisation methods, including deterministic and stochastic algorithms. Extensive simulation using Matlab environment in conjuction with measured data has been carried out to show and choose the most suitable and efficient algorithms for a given optimisation task. The analysis results for Cuckoo Search are compared with those obtained by Particle Swarm Optimisation , Convex hull, Improved Convex hull, and Least squares. The implementation proves that the nature-inspired optimisation algorithms outperform traditional algorithms with good global convergence capability and can act as an alternative optimisation method for automated manufacturing problems.