The performance of algorithms for decentralized optimization is affected by both the optimization error and the consensus error, the latter of which arises from the variation between agents' local models. Classica...
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The problem of optimizing trajectory tracking algorithms is considered. Based on measurements of a moving object, such algorithms iteratively make estimates of its state. These algorithms contain parameters that affec...
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Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle un...
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Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is at
Due to the lack of management capability of the model development process on traditional experiment platforms, they cannot meet the continuous experimentation, reproducibility, and traceability needs of university res...
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In this paper, an improved genetic simulated annealing algorithm (SAGA) for wireless sensor networks is studied and designed. The aim of this project is to better study the optimal transmission path in wireless sensor...
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In the modern context, the concept of optimal path in logistics distribution has gone beyond the simple shortest geographical distance, but covers more diversified optimization objectives. It may involve minimizing tr...
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We investigate the distributed stochastic optimization by nodes over the uncertain communication topologies to cooperatively minimize a sum of strongly convex local cost functions. The communication topologies are des...
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Our study focuses on the development of new Estimation of Distribution algorithms (EDAs) with neuro-evolution for pseudo-Boolean optimization problems. We define a strategy for updating the frequency vector at each ge...
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This comprehensive review explores a range of optimization approaches for Combined Economic Emission Dispatch (CEED), covering conventional, non-conventional, and hybrid techniques. CEED is critical in minimizing econ...
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This comprehensive review explores a range of optimization approaches for Combined Economic Emission Dispatch (CEED), covering conventional, non-conventional, and hybrid techniques. CEED is critical in minimizing economic costs and emissions while ensuring power system reliability. Traditional methods focus on cost minimization but overlook environmental considerations. optimization techniques address this gap by simultaneously optimizing economic and environmental objectives. Hybrid techniques, combining multiple algorithms or integrating renewable energy, further enhance CEED performance. The review evaluates these approaches' strengths and limitations, considering factors like computational efficiency and solution accuracy. Over the past few decades, a great deal of study has been done on the use of renewable energy (RE) as an alternative source in power generation systems. As a result, the power dispatch problem currently uses the Combined Economic Emission Dispatch (CEED) of thermal and renewable energy resources. It discusses the potential of hybrid techniques and take in consideration renewable energy integration in achieving cost savings and emission reductions, highlighting areas for future research.
作者：
Amala, K.J.Rajeswari, D.School of Computing
College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India
Feature Selection (FS) is essential for optimizing Learning to Rank (LTR) models by determining the vital choice of attributes from a complex dataset. This study examines feature selection strategies within the framew...
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