Experimental studies of eddy currents

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Optimization of Turning Operations by Using a Hybrid Genetic Algorithm with Sequential Quadratic Programming

The determination of optimal cutting parameters is one of the most important elements in any process planning ofmetal parts. In this paper, a new hybrid genetic algorithm by using sequential quadratic programming is used for theoptimization of cutting conditions. It is used for the resolution of a multipass turning optimization case by minimizingthe production cost under a set of machining constraints. The genetic algorithm (GA) is the main optimizer of thisalgorithm whereas SQP Is used to fine tune the results obtained from the GA. Furthermore, the convergencecharacteristics and robustness of the proposed method have been explored through comparisons with resultsreported in literature. The obtained results indicate that the proposed hybrid genetic algorithm by using a sequentialquadratic programming is effective compared to other techniques carried out by different researchers

Globally convergence of nonlinear conjugate gradient method for unconstrained optimization

The conjugate gradient method is a useful and powerful approach for solving large-scale minimization problems. In this paper, a new nonlinear conjugate gradient method is proposed for large-scale unconstrained optimization. This method include the already existing two practical nonlinear conjugate gradient methods, to combine the nice global convergence properties of Fletcher-Reeves method (abbreviated FR) and the good numerical performances of the Polak–Ribiére–Polyak method (abbreviated PRP), which produces a descent search direction at every iteration and converges globally provided that the line search satisfies the Wolfe conditions. Our numerical results show that of the new method is very efficient for the given test problems. In addition we will study the methods related to the new nonlinear conjugate gradient method