In this paper, a new robust approach based on Least Square Support Vector Machine (LSSVM) as a proxy model is used for an automatic fractured reservoir history matching. The proxy model is made to model the history match objective function (mismatch values) based on the history data of the field. This model is then used to minimize the objective function through Particle Swarm Optimization (...

In traditional scheduling literature, it is generally assumed that the location of facilities are predetermined and fixed in advance. However, these decisions are interrelated and may impact each other significantly. Therefore finding a schedule and facility location has become an important problem as an extension of the well-known scheduling problems. In this research we consider joint decisio...

In this paper we introduce a tool aimed at assisting composers in orchestration tasks. Thanks to this tool, composers can specify a target sound and replicate it with a given orchestra. We discuss the problems raised by the realization of such a tool, concerning instrumental sound description and combinatorial optimization. Then we describe the solution adopted. We propose a machine learning me...

In this paper, a consistent pattern with the optimal coordinated design of PSS and SSSC controller to improve the damping of low frequency oscillations is shown. In this design, sensing and signal transmission time delays are considered as effectiveness parameters. The design problem has been considered an optimization problem and biogeography-based optimization (BBO) algorithm is used for sear...

This work proposes a process planning for machining of a Floor which is the most prominent elemental machining feature in a 21⁄2D pocket. Traditionally, the process planning of 21⁄2D pocket machining is posed as stand-alone problem involving either tool selection, tool path generation or machining parameter selection, resulting in sub-optimal plans. For this reason, the tool path generation and...

this paper considers identical parallel machines scheduling problem with past-sequence-dependent setup times, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of the processing times of the jobs already processed and its scheduled position on the corresponding machine. in addition, the setup time of a job on each machin...

Bayesian optimization is a powerful tool for finetuning the hyper-parameters of a wide variety of machine learning models. The success of machine learning has led practitioners in diverse real-world settings to learn classifiers for practical problems. As machine learning becomes commonplace, Bayesian optimization becomes an attractive method for practitioners to automate the process of classif...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...