The nature has inspired several metaheuristics, outstanding among these is Ant Colony Optimization(ACO), which have proved to be very effective and efficient in problems of high complexity (NP-hard) incombinatorial optimization. This paper describes the implementation of an ACO model algorithm known asElitist Ant System (EAS), applied to a combinatorial optimization problem called Job Shop SchedulingProblem (JSSP). We propose a method that seeks to reduce delays designating the operation immediatelyavailable, but considering the operations that lacklittle to be available and have a greater amount ofpheromone. The performance of the algorithm was evaluated for problems of JSSP reference, comparingthe quality of the solutions obtained regarding thebest known solution of the most effective methods.Thesolutions were of good quality and obtained with aremarkable efficiency by having to make a very lownumber of objective function evaluations
We present a gradient-based optimization method for the estimation of a specimen's phase function from polychromatic DIC images. The method minimizes the sum of a nonlinear least-squares discrepancy measure and a smooth approximation of the total variation. A new formulation of the gradient and a recent updating rule for the choice of the step size are both exploited to reduce computational time. Numerical simulations on two computer-generated objects show significant improvements, both in efficiency and accuracy, with respect to a more standard choice of the step size.
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