Displaying similar documents to “Expériences with Stochastic Algorithms fir a class of Constrained Global Optimisation Problems”

A Global Stochastic Optimization Method for Large Scale Problems

W. El Alem, A. El Hami, R. Ellaia (2010)

Mathematical Modelling of Natural Phenomena

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In this paper, a new hybrid simulated annealing algorithm for constrained global optimization is proposed. We have developed a stochastic algorithm called ASAPSPSA that uses Adaptive Simulated Annealing algorithm (ASA). ASA is a series of modifications to the basic simulated annealing algorithm (SA) that gives the region containing the global solution of an objective function. In addition, Simultaneous Perturbation Stochastic Approximation...

A new nonmonotone adaptive trust region algorithm

Ahmad Kamandi, Keyvan Amini (2022)

Applications of Mathematics

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We propose a new and efficient nonmonotone adaptive trust region algorithm to solve unconstrained optimization problems. This algorithm incorporates two novelties: it benefits from a radius dependent shrinkage parameter for adjusting the trust region radius that avoids undesirable directions and exploits a new strategy to prevent sudden increments of objective function values in nonmonotone trust region techniques. Global convergence of this algorithm is investigated under some mild...

Parallelization of artificial immune systems using a massive parallel approach via modern GPUs

Khun, Jiří, Šimeček, Ivan

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Parallelization is one of possible approaches for obtaining better results in terms of algorithm performance and overcome the limits of the sequential computation. In this paper, we present a study of parallelization of the opt-aiNet algorithm which comes from Artificial Immune Systems, one part of large family of population based algorithms inspired by nature. The opt-aiNet algorithm is based on an immune network theory which incorporates knowledge about mammalian immune systems in...