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Abstract This paper introduces an automatic strategy for the segmentation of medical images from Magnetic Resonance Imaging (MRI) and Computed Topography (CT). A new segmentation technique is proposed to combine a new evolutionary algorithm, called the Immune System Programming (ISP) algorithm, with the Region Growing (RG) technique. The ISP algorithm with a tree data structure has the ability to create new mathematical threshold functions, and RG can use these functions to achieve an efficient segmentation process for medical images. Several MRI images with different levels of Radio Frequency (RF) and noise are used to test the proposed segmentation technique. In different experiments, the proposed technique showed promising performance and produced a new set of efficient threshold functions. [ABSTRACT FROM AUTHOR]

Cloud computing is currently playing an important role in different applications. Deploying graphical processing units (GPUs) as a resource in clouds constitutes a major challenge. As the number and the scale of internet services increase, the services running on a cloud computing environment also increase. These services could have been efficiently improved in terms of a service level agreement (SLA) if we could make use of the GPUs architectures in the cloud infrastructure. Cloud providers currently offer GPUs as an infrastructure as a service (IaaS). Providing GPUs in another context such as software as a service (SaaS) requires a resource allocation algorithm. This paper proposes a resource allocation algorithm (RAA) for GPUs in the cloud environment. The proposed RAA will provide a way for cloud providers to offer GPUs in a SaaS context. The RAA is based on the load balancing mechanism trying to fully utilise all the GPUs resources. This paper also investigates the suitability of the RAA by conducting a video enhancement service on a private cloud. It shows that the proposed algorithm provides scalability and enhancements in terms of a SLA.

In this paper, a simulated-annealing-based method called Filter Simulated Annealing (FSA) method is proposed to deal with the constrained global optimization problem. The considered problem is reformulated so as to take the form of optimizing two functions, the objective function and the constraint violation function. Then, the FSA method is applied to solve the reformulated problem. The FSA method invokes a multi-start diversification scheme in order to achieve an efficient exploration process. To deal with the considered problem, a filter-set-based procedure is built in the FSA structure. Finally, an intensification scheme is applied as a final stage of the proposed method in order to overcome the slow convergence of SA-based methods. The computational results obtained by the FSA method are promising and show a superior performance of the proposed method, which is a point-to-point method, against population-based methods.In this paper, a simulated-annealing-based method called Filter Simulated Annealing (FSA) method is proposed to deal with the constrained global optimization problem. The considered problem is reformulated so as to take the form of optimizing two functions, the objective function and the constraint violation function. Then, the FSA method is applied to solve the reformulated problem. The FSA method invokes a multi-start diversification scheme in order to achieve an efficient exploration process. To deal with the considered problem, a filter-set-based procedure is built in the FSA structure. Finally, an intensification scheme is applied as a final stage of the proposed method in order to overcome the slow convergence of SA-based methods. The computational results obtained by the FSA method are promising and show a superior performance of the proposed method, which is a point-to-point method, against population-based methods.

In this article we give a new approach of hybrid direct search methods with meta-heuristics of simulated annealing for finding a global minimum of a nonlinear function with continuous variables. First, we suggest a Simple Direct Search (SDS) method, which comes from some ideas of other well-known direct search methods. Since our goal is to find global minima and the SDS method is still a local search method, we hybridize it with the standard simulated annealing to design a new method, called Simplex Simulated Annealing (SSA) method, which is expected to have some ability to look for a global minimum. To obtain faster convergence, we first accelerate the cooling schedule in SSA, and in the final stage, we apply Kelley's modification of the Nelder-Mead method on the best solutions found by the accelerated SSA method to improve the final results. We refer to this last method as the Direct Search Simulated Annealing (DSSA) method. The performance of SSA and DSSA is reported through extensive numerical experiments on some well-known functions. Comparing their performance with that of other meta-heuristics shows that SSA and DSSA are promising in practice. Especially, DSSA is shown to be very efficient and robust.