This paper presents a new hybrid approach integrating the cross-entropy (CE) algorithm and the sequential quadratic programming (SQP) technique to solve the economic load dispatch (ELD) problem related to electrical power generating units. Due to the introduction of the valve-point effect in the ELD objective function, the optimization task requires tools appropriate for a nonconvex optimization landscape. In this respect, we employ the CE approach, which constructs a random sequence of solutions probabilistically converging to a near-optimal solution and, thus, facilitating the exploration capability. Additionally, to fine-tune the solution in promising basins of attraction, the SQP algorithm is invoked, which performs a local search. Despite its reliance on a global heuristic scheme, CE-SQP is vested with fast convergence capability, which may entail its use for online power dispatch. The effectiveness and the robustness of the proposed method in comparison with several state-of-the-art approaches have been demonstrated with four standard test systems that are widely reported in the ELD literature.

Membrane Computing is a biologically inspired computational model. Its devices are called P systems and they perform computations by applying a finite set of rules in a synchronous, maximally parallel way. In this paper, we open a new research line: P systems are used in Computational Topology within the context of the Digital Image. We choose for this a variant of P systems, called tissue-like P systems, to obtain in a general maximally parallel manner the segmentation of 2D and 3D images in a constant number of steps. Finally, we use a software called Tissue Simulator to check these systems with some examples.

This paper presents a comprehensive review of two major image processing tasks such as restoration and segmentation in the medical field on deep learning perspective. These processes are essential as restoration helps in the removal of noise and segmentation helps in extracting the particular region of interest of an image which is essential for accurate diagnosis and treatment. This paper mainly focuses on deep learning techniques as it plays a prominent role over other conventional techniques in handling a large number of datasets in the medical field and also provides accurate results. In this paper, we reviewed the application of different convolutional neural network architectures in the restoration and segmentation processes. Based on the results in the case of image restoration, TLR-CNN and Stat-CNN are promising in achieving better PSNR, noise suppression, artifact suppression and improves the overall image quality. For segmentation process, LCP net achieves the Dice score as 98.12% and sensitivity as 98.95% in the cell contour segmentation;3D FCNN model is found to be the best method for segmentation of brain tumors. This review work shows that deep learning methodologies can be a better alternative for medical image restoration and segmentation tasks as the data size is an important concern as on today.