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Multiobjective Optimization Techniques Applied to Three-Phase Transformers Designs

Design and development of learning model for compression and processing of deoxyribonucleic acid genome sequence.

Owing to the substantial volume of human genome sequence data files (from 30-200 GB exposed) Genomic data compression has received considerable traction and storage costs are one of the major problems faced by genomics laboratories. This involves a modern technology of data compression that reduces not only the storage but also the reliability of the operation. There were few attempts to solve this problem independently of both hardware and software. A systematic analysis of associations between genes provides techniques for the recognition of operative connections among genes and their respective yields, as well as understandings into essential biological events that are most important for knowing health and disease phenotypes. This research proposes a reliable and efficient deep learning system for learning embedded projections to combine gene interactions and gene expression in prediction comparison of deep embeddings to strong baselines. In this paper we preform data processing operations and predict gene function, along with gene ontology reconstruction and predict the gene interaction. The three major steps of genomic data compression are extraction of data, storage of data, and retrieval of the data. Hence, we propose a deep learning based on computational optimization techniques which will be efficient in all the three stages of data compression.

Identification study of solar cell/module using recent optimization techniques

This paper proposes the application of a novel metaphor-free population optimization based on the mathematics of the Runge Kutta method (RUN) for parameter extraction of a double-diode model of the unknown solar cell and photovoltaic (PV) module parameters. The RUN optimizer is employed to determine the seven unknown parameters of the two-diode model. Fitting the experimental data is the main objective of the extracted unknown parameters to develop a generic PV model. Consequently, the root means squared error (RMSE) between the measured and estimated data is considered as the primary objective function. The suggested objective function achieves the closeness degree between the estimated and experimental data. For getting the generic model, applications of the proposed RUN are carried out on two different commercial PV cells. To assess the proposed algorithm, a comprehensive comparison study is employed and compared with several well-matured optimization algorithms reported in the literature. Numerical simulations prove the high precision and fast response of the proposed RUN algorithm for solving multiple PV models. Added to that, the RUN can be considered as a good alternative optimization method for solving power systems optimization problems.

Parameter estimation of positive lightning impulse using curve fitting-based optimization techniques and least squares algorithm

Deep convolutional neural network-based system for fish classification.

<p>In computer vision, image classification is one of the potential image processing tasks. Nowadays, fish classification is a wide considered issue within the areas of machine learning and image segmentation. Moreover, it has been extended to a variety of domains, such as marketing strategies. This paper presents an effective fish classification method based on convolutional neural networks (CNNs). The experiments were conducted on the new dataset of Bangladesh’s indigenous fish species with three kinds of splitting: 80-20%, 75-25%, and 70-30%. We provide a comprehensive comparison of several popular optimizers of CNN. In total, we perform a comparative analysis of 5 different state-of-the-art gradient descent-based optimizers, namely adaptive delta (AdaDelta), stochastic gradient descent (SGD), adaptive momentum (Adam), adaptive max pooling (Adamax), Root mean square propagation (Rmsprop), for CNN. Overall, the obtained experimental results show that Rmsprop, Adam, Adamax performed well compared to the other optimization techniques used, while AdaDelta and SGD performed the worst. Furthermore, the experimental results demonstrated that Adam optimizer attained the best results in performance measures for 70-30% and 80-20% splitting experiments, while the Rmsprop optimizer attained the best results in terms of performance measures of 70-25% splitting experiments. Finally, the proposed model is then compared with state-of-the-art deep CNNs models. Therefore, the proposed model attained the best accuracy of 98.46% in enhancing the CNN ability in classification, among others.</p>

Optimization Techniques to Solve Travelling Salesman Problem Using Machine Learning Algorithms

Abstract: Travelling Salesmen problem is a very popular problem in the world of computer programming. It deals with the optimization of algorithms and an ever changing scenario as it gets more and more complex as the number of variables goes on increasing. The solutions which exist for this problem are optimal for a small and definite number of cases. One cannot take into consideration of the various factors which are included when this specific problem is tried to be solved for the real world where things change continuously. There is a need to adapt to these changes and find optimized solutions as the application goes on. The ability to adapt to any kind of data, whether static or ever-changing, understand and solve it is a quality that is shown by Machine Learning algorithms. As advances in Machine Learning take place, there has been quite a good amount of research for how to solve NP-hard problems using Machine Learning. This reportis a survey to understand what types of machine algorithms can be used to solve with TSP. Different types of approaches like Ant Colony Optimization and Q-learning are explored and compared. Ant Colony Optimization uses the concept of ants following pheromone levels which lets them know where the most amount of food is. This is widely used for TSP problems where the path is with the most pheromone is chosen. Q-Learning is supposed to use the concept of awarding an agent when taking the right action for a state it is in and compounding those specific rewards. This is very much based on the exploiting concept where the agent keeps on learning onits own to maximize its own reward. This can be used for TSP where an agentwill be rewarded for having a short path and will be rewarded more if the path chosen is the shortest. Keywords: LINEAR REGRESSION, LASSO REGRESSION, RIDGE REGRESSION, DECISION TREE REGRESSOR, MACHINE LEARNING, HYPERPARAMETER TUNING, DATA ANALYSIS

Designing a locating-routing three-echelon supply chain network under uncertainty

PurposeIn the present research, location and routing problems, as well as the supply chain, which includes manufacturers, distributor candidate sites and retailers, are explored. The goal of addressing the issue is to reduce delivery times and system costs for retailers so that routing and distributor location may be determined.Design/methodology/approachBy adding certain unique criteria and limits, the issue becomes more realistic. Customers expect simultaneous deliveries and pickups, and retail service start times have soft and hard time windows. Transportation expenses, noncompliance with the soft time window, distributor construction, vehicle purchase or leasing, and manufacturing costs are all part of the system costs. The problem's conceptual model is developed and modeled first, and then General Algebraic Modeling System software (GAMS) and Multiple Objective Particle Swarm Optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGAII) algorithms are used to solve it in small dimensions.FindingsAccording to the mathematical model's solution, the average error of the two suggested methods, in contrast to the exact answer, is less than 0.7%. In addition, the performance of algorithms in terms of deviation from the GAMS exact solution is pretty satisfactory, with a divergence of 0.4% for the biggest problem (N = 100). As a result, NSGAII is shown to be superior to MOSPSO.Research limitations/implicationsSince this paper deals with two bi-objective models, the priorities of decision-makers in selecting the best solution were not taken into account, and each of the objective functions was given an equal weight based on the weighting procedures. The model has not been compared or studied in both robust and deterministic modes. This is because, with the exception of the variable that indicates traffic mode uncertainty, all variables are deterministic, and the uncertainty character of demand in each level of the supply chain is ignored.Practical implicationsThe suggested model's conclusions are useful for any group of decision-makers concerned with optimizing production patterns at any level. The employment of a diverse fleet of delivery vehicles, as well as the use of stochastic optimization techniques to define the time windows, demonstrates how successful distribution networks are in lowering operational costs.Originality/valueAccording to a multi-objective model in a three-echelon supply chain, this research fills in the gaps in the link between routing and location choices in a realistic manner, taking into account the actual restrictions of a distribution network. The model may reduce the uncertainty in vehicle performance while choosing a refueling strategy or dealing with diverse traffic scenarios, bringing it closer to certainty. In addition, two modified MOPSO and NSGA-II algorithms are presented for solving the model, with the results compared to the exact GAMS approach for medium- and small-sized problems.

Optimal PID Tuning of PLL for PV Inverter Based on Aquila Optimizer

Phase-locked loop (PLL) is a fundamental and crucial component of a photovoltaic (PV) connected inverter, which plays a significant role in high-quality grid connection by fast and precise phase detection and lock. Several novel critical structure improvements and proportional-integral (PI) parameter optimization techniques of PLL were proposed to reduce shock current and promote the quality of grid connection at present. However, the present techniques ignored the differential element of PLL and did not acquire ideal results. Thus, this paper adopts Aquila optimizer algorithm to regulate the proportional-integral-differential (PID) parameters of PLL for smoothing power fluctuation and improving grid connection quality. Three regulation strategies (i.e., PLL regulation, global regulation, and step regulation) are carefully designed to systematically and comprehensively evaluate the performance of the proposed method based on a simulation model in MATLAB/Simulink, namely, “250-kW Grid-Connected PV Array”. Simulation results indicate that PLL regulation strategy can effectively decrease power fluctuation and overshoot with a short response time, low complexity, and time cost. Particularly, the Error(P) and the maximum deviation of output power under optimal parameters obtained by PLL strategy are decreased by 418 W and 12.5 kW compared with those under initial parameters, respectively.

Principles for an Implementation of a Complete CT Reconstruction Tool Chain for Arbitrary Sized Data Sets and Its GPU Optimization

This article describes the implementation of an efficient and fast in-house computed tomography (CT) reconstruction framework. The implementation principles of this cone-beam CT reconstruction tool chain are described here. The article mainly covers the core part of CT reconstruction, the filtered backprojection and its speed up on GPU hardware. Methods and implementations of tools for artifact reduction such as ring artifacts, beam hardening, algorithms for the center of rotation determination and tilted rotation axis correction are presented. The framework allows the reconstruction of CT images of arbitrary data size. Strategies on data splitting and GPU kernel optimization techniques applied for the backprojection process are illustrated by a few examples.

Technical note: Graph theory-based heuristics to aid in the implementation of optimized drinking water network sectorization

Abstract. Drinking water distribution networks form an essential part of modern-day critical infrastructure. Sectorizing a network into district metered areas is a key technique for pressure management and water loss reduction. Sectorizing an existing network from scratch is, however, an exceedingly complex design task that designs in a well-studied general mathematical problem. Numerical optimization techniques such as evolutionary algorithms can be used to search for near-optimal solutions to such problems, but doing so within a reasonable timeframe remains an ongoing challenge. In this work, we introduce two heuristic tricks that use information of the network structure and information of the operational requirements of the drinking water distribution network to modify the basic evolutionary algorithm used to solve the general problem. These techniques not only reduce the time required to find good solutions, but also ensure that these solutions better match the requirements of drinking water practice. Both techniques were demonstrated by applying them in the sectorization of the actual distribution network of a large city.

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A Comparative Analysis of Optimization Techniques

• December 2015
• International Journal of Computer Applications 131(10):6-12
• 131(10):6-12
• This person is not on ResearchGate, or hasn't claimed this research yet.

• INHA university in Tashkent, Uzbekistan

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Research on Real-Time Coordinated Optimization Scheduling Control Strategy with Supply-Side Flexibility in Multi-Microgrid Energy Systems

36 Pages Posted: 31 Aug 2024

Guizhou Normal University

Zhipeng Wang

Guizhou University

Jinqing Linghu

Yupeng qiao.

With the increasing interconnection of regional microgrids, the full utilization of energy and stable operation of the system have become the current research hotspots. Meanwhile, the optimal coordination of energy in multiple microgrids under stable operation has become a highly challenging problem. Taking into account the diversity and complementarity of energy sources within the system, this paper proposes a multi-microgrid energy complementation model by fully considering the flexibility characteristics of the supply side, and puts forward a real-time optimization dispatch control strategy based on the model. This strategy takes a completely new approach, based on the characteristics of different types of energy, dividing the dispatch process into two parts to coordinate the energy interactions among multiple microgrids, and then determining the optimal dispatching scheme for each part to ensure the autonomous stable operation of individual MGs while maximizing the self-sufficiency of the MMG system. At the same time, a case study was conducted on the IEEE 33 node model. Simulation results show that the proposed control strategy achieves the full integration of renewable energy in the multi-microgrid system, reduces system operating costs, and achieves the optimal coordination of energy in multi-microgrid systems.

Keywords: Multi-microgrid, Energy complementarity, Energy interaction, Real-time optimization, Optimal collaboration

Suggested Citation: Suggested Citation

Nan Wu (Contact Author)

Guizhou normal university ( email ), guizhou university ( email ).

Guizhou China

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Current research status and future trends of vibration energy harvesters.

1. Introduction

2. electromagnetic vibration energy harvester, 2.1. working principle and characteristics of the electromagnetic vibration energy harvester, 2.2. advances in electromagnetic vibration energy harvesters, 3. piezoelectric vibration energy harvester, 3.1. operating principle and characteristics of piezoelectric vibration energy harvester, 3.2. progress of research on piezoelectric vibration energy harvesters, 4. friction electric vibration energy harvester, 4.1. mechanism of operation and characteristics of the friction electric vibration energy harvesters, 4.1.1. friction nanogenerator, 4.1.2. principle of friction electric vibration energy harvester, 4.2. advances in friction electric vibration energy harvesters, 5. electrostatic vibration energy harvester, 5.1. the working principle of the electrostatic vibration energy harvester and its characteristics, 5.2. current research status of electrostatic vibration energy harvesters, 6. magnetostrictive vibration energy harvester, 6.1. operating principle and characteristics of magnetostrictive vibration energy harvesters, 6.2. current status of research on magnetostrictive vibration energy harvesters, 7. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Qu, G.; Xia, H.; Liang, Q.; Liu, Y.; Ming, S.; Zhao, J.; Xia, Y.; Wu, J. Current Research Status and Future Trends of Vibration Energy Harvesters. Micromachines 2024 , 15 , 1109. https://doi.org/10.3390/mi15091109

Qu G, Xia H, Liang Q, Liu Y, Ming S, Zhao J, Xia Y, Wu J. Current Research Status and Future Trends of Vibration Energy Harvesters. Micromachines . 2024; 15(9):1109. https://doi.org/10.3390/mi15091109

Qu, Guohao, Hui Xia, Quanwei Liang, Yunping Liu, Shilin Ming, Junke Zhao, Yushu Xia, and Jianbo Wu. 2024. "Current Research Status and Future Trends of Vibration Energy Harvesters" Micromachines 15, no. 9: 1109. https://doi.org/10.3390/mi15091109

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A MIP-heuristic approach for solving a bi-objective optimization model for integrated production planning of sugarcane and energy-cane

• Original Research
• Published: 02 September 2024

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• Gilmar Tolentino 1   na1 ,
• Antônio Roberto Balbo   ORCID: orcid.org/0000-0002-4512-0140 1   na1 ,
• Sônia Cristina Poltroniere 1   na1 ,
• Angelo Aliano Filho   ORCID: orcid.org/0000-0002-5088-134X 2   na1 &
• Helenice de Oliveira Florentino   ORCID: orcid.org/0000-0003-2740-8826 3   na1  

This paper proposes a modeling and solution approach for the integrated planning of the planting and harvesting of sucrose cane and energy-cane considering multiple harvesters. An integer linear bi-objective optimization model is proposed, which seeks to find a trade-off between the maximization of the production volumes of sucrose and fiber and the minimization of the operational costs. The model considers the technical constraints of the mill, such as the milling capacity and meeting the monthly demand. A MIP-heuristic based on relax-and-fix and fix-and-optimize strategies with exact decomposition is appropriately proposed to determine approximations to Pareto optimal solutions to this problem. These approximations are used as incumbents for a branch-and-bound tree to generate potentially Pareto optimal solutions. The results reveal that the MIP-heuristic efficiently solves the problem for real and semi-random instances, generating approximate solutions with a reduced error and a reasonable computational effort. Moreover, the different solutions quantify the trade-off between cost and production volume, opening up the possibility of increasing sucrose and fiber content or decreasing the costs of solutions found. Thus, the proposed bi-objective approach, the solution technique and the different Pareto optimal solutions obtained can assist mill managers in making better decisions in sugarcane production.

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The authors thank to Brazilian foundations: CNPq n \(^{\textrm{o}}\) 306518/2022-8, CNPq n \(^{\textrm{o}}\) 304218/2022-7, FAPESP 2021/03039-1,FAPESP 2022/12652-1, PROPE/PROPG/UNESP/ FUNDUNESP grant 12/2022, for the financial support and language services provided.

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Gilmar Tolentino, Antônio Roberto Balbo, Sônia Cristina Poltroniere, Angelo Aliano Filho and Helenice de Oliveira Florentino have contributed equally to this work.

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Department of Mathematics, State University of Sao Paulo, Bauru, São Paulo, 17033-360, Brazil

Gilmar Tolentino, Antônio Roberto Balbo & Sônia Cristina Poltroniere

Department of Mathematics, Universidade Tecnológica Federal do Paraná, Apucarana, Paraná, 86812-460, Brazil

Angelo Aliano Filho

Department of Bioestatistics, State University of Sao Paulo, Botucatu, São Paulo, 18618-690, Brazil

Helenice de Oliveira Florentino

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Tolentino, G., Balbo, A.R., Poltroniere, S.C. et al. A MIP-heuristic approach for solving a bi-objective optimization model for integrated production planning of sugarcane and energy-cane. Ann Oper Res (2024). https://doi.org/10.1007/s10479-024-06229-5

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DOI : https://doi.org/10.1007/s10479-024-06229-5

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