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Designing a Squared-Off Cascade for Separation of the ⁹²Mo and ¹⁰⁰Mo Isotopes to High Purity

JOURNAL OF ENGINEERING PHYSICS AND THERMOPHYSICS 2024年第4期97卷 830-843页

作者： Mirmohammadi, S. L. Safdari, J. Mallah, M. H. Ezazi, F. Atom Energy Org Nucl Sci & Technol Res Inst Mat & Nucl Fuel Res Sch POB 11365-8486 Tehran Iran

The separation of the Mo-92 and Mo-100 isotopes from the natural composition to the 99.99% enrichment level in a three-section squared-off cascade with 200 centrifuges was investigated. The parameters of this cascade were optimized using the 3SQC-PSO and 3SQC-GOA codes and the PSO and GO algorithms. The objective function of the indicated optimization codes is to simultaneously maximize the cascade capacity, the recovery factor, and the D-function. The results obtained show that the separation of the Mo-92 and Mo-100 isotopes should be done in two and one separation steps, respectively. A single cascade of most suitable configuration for the separation of both the Mo-92 and Mo-100 isotopes is proposed. The recovery separations of the Mo-92 and Mo-100 isotopes in this cascade comprise 92 and 95%, respectively.

关键词： lightest and heaviest molybdenum isotopes Mo-92 and Mo-100 three-section squared-off cascade particle swarm optimization algorithm grasshopper optimization algorithm

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Supplier selection and order allocation model with disruption and environmental risks in centralized supply chain

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2021年第6期12卷 1036-1072页

作者： Rezaei, Aida Aghsami, Amir Rabbani, Masoud Univ Tehran Coll Engn Sch Ind & Syst Engn Tehran Iran KN Toosi Univ Technol Sch Ind Engn Tehran Iran

Supply chain managers have realized that competition between supply chains has replaced competition between companies. In addition, with increasing disruptions and uncertainty in planning, companies need to be able to make informed decisions at risk. Coordination in the resilient supply chain and appropriate selection of suppliers play a key role in risky situations. In previous research, mainly the impact of resilience strategies in the decentralized supply chain has been investigated and ignored the reliability of suppliers in the decision-making process. Therefore, we provide an effective framework for selecting reliable suppliers and order allocation, which increases the supply chain's benefits by considering the risk reduction strategies and coordination between the buyer and the supplier. Thus, we optimized the problem of supplier selection and order allocation in a centralized supply chain using mixed-integer nonlinear programming models and risk reduction strategies. These strategies are protected suppliers, back-up suppliers, reserving additional capacity, emergency stock, and geographical separation. Also, by considering the failure mode and effects analysis technique and the risk priority number constraint, suppliers' reliability has been considered. A numerical example is solved with the exact method. In addition, the application of the proposed models in a case study has been investigated by the grasshopper optimization algorithm. Based on the sensitivity analysis results, we found that the simultaneous use of risk reduction strategies in the models significantly reduces supply chain costs and increases its benefits. Also, considering the reliability constraints causes supply chain managers to choose suppliers with more desirable reliability.

关键词： Resilient supply chain Supplier selection and order allocation Disruption risks Nonlinear mixed-integer programming Failure mode and effects analysis grasshopper optimization algorithm

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A Competitive Swarm algorithm for Image Segmentation Guided by Opposite Fuzzy Entropy

A Competitive Swarm Algorithm for Image Segmentation Guided ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Abd Elaziz, Mohamed Ewees, Ahmed A. Yousri, Dalia Oliva, Diego Lu, Songfeng Cuevas, Erik Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Damietta Univ Dept Comp Dumyat Egypt Fayoum Univ Fac Engn Dept Elect Engn Al Fayyum Egypt Univ Guadalajara CUCEI Dept Ciencias Computac Guadalajara Jalisco Mexico Univ Oberta Catalunya Comp Sci Dept IN3 Castelldefels Spain Nanjing Souwen Informat Technol Nanjing 211800 Peoples R China

ISBN: (纸本)9781728169323

This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.

关键词： Opposite fuzzy set Image segmentation grasshopper optimization algorithm Sine-cosine algorithm

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HandWave: An EMG-Powered System for Intuitive Gesture Recognition

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SN Computer Science 2024年第8期5卷 1038页

作者： Agarwal, Shweta Kaur, Bobbinpreet Bhati, Bhoopesh Singh Chandigarh University Punjab Mohali 140413 India Indian Institute of Information Technology Haryana Sonepat 131001 India

Hand gestures are non-verbal communication techniques that use specific movements of hand, finger, and wrist postures to express ideas, feelings, or instructions. The classification of hand gestures accurately is challenging due to the unpredictability and large dimensionality of the data. The main objective of this study is to develop a classification model based on the EMG signals, which will identify the most significant features in order to optimize the classification accuracy. The proposed framework utilizes the MYO thalmic bracelet recording dataset. The process comprises two stages: the initial step includes extracting features, while the second stage proposes a grasshopper (GH)-based feature selection algorithm that integrates swarm intelligence (SI) and machine learning (ML) for the optimal selection of features. Thereafter, the best set of features is trained and categorized for hand gestures using classifiers like Decision Trees (DT), K-nearest neighbor (KNN), Artificial Neural Networks (ANN), Naive Bayes (NB), and ensemble bagged trees to see how well it does in terms of F-measure, precision, recall and accuracy. Overall, simulation results revealed that the proposed work based on the GH optimization algorithm, provides 99.98% accuracy using the KNN classifier and outperformed other classifiers in predicting hand gestures. This study has successfully proposed a robust EMG-based hand gesture classification model with high accuracy using the GH feature selection method. These results emphasized the capability of the KNN classifier in recognizing hand gestures and further extended valuable insights into the techniques of non-verbal communication and their applications in medical diagnostics and biomechanics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electromyography grasshopper optimization algorithm Hand Gesture Machine Learning

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Hyper-heuristic strategies for optimal power flow problem with FACTS devices allocation in wind power integrated system

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RESULTS IN CONTROL AND optimization 2024年 14卷

作者： Sulaiman, Mohd Herwan Mustaffa, Zuriani Univ Malaysia Pahang Al Sultan Abdullah UMPSA Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia Univ Malaysia Pahang Al Sultan Abdullah UMPSA Fac Comp Pekan 26600 Pahang Malaysia

This research provides hyper-heuristic methodologies for solving Optimal Power Flow (OPF) issues in power system networks with Flexible AC Transmission Systems (FACTS) devices. OPF can be treated as one of the demanding challenges in the power system operating networks. To address the problems of loss and cost reduction, three types of FACTS devices will be studied in this paper: Static VAR Compensator (SVC), ThyristorControlled Series Compensator (TCSC), and Thyristor-Controlled Phase Shifter (TCPS). Two high level hyper-heuristic (HHH) approaches, called Exponential Monte Carlo with counter (EMCQ) and randomly select-Only Improving (OI), are employed as high-level metaheuristic to select and leverage the effectiveness of four low-level metaheuristics (LLH). These low-level metaheuristics comprise the Moth-Flame Optimizer (MFO), Barnacles Mating Optimizer (BMO), Teaching-Learning Based optimization (TLBO) and Gradient-Based Optimizer (GBO). The usage of HHH solving the OPF problem is tested on the modified IEEE 30 bus system that integrates the thermal generators with the wind power. Findings of the study demonstrated the promising results by HHH which manages to outperform all the selected LLH algorithms.

关键词： ABC artificial bee colony AGPSO GWO autonomous groups particle swarm and grey wolf optimizers ANN artificial neural network BF bacterial foraging BMO Barnacles Mating Optimizer DE differential evolution DG distributed generation EMCQ exponential Monte Carlo with counter FACTS flexible AC transmission system GBO gradient-based optimizer GSA gravitational search algorithm GOA grasshopper optimization algorithm HBO heap-based optimization HHH high level hyper-heuristic IP interior point LLH low-level heuristic LP linear programming MFO moth flame optimization MLAPO modified lightning attachment procedure optimizer NLP non-linear programming NM Nelder–mead OI randomly select-only improving OPF optimal power flow PSO particle swarm optimization PV photovoltaic QP quadratic programming SSA Salp swarm algorithm SHADE success history based adaptive DE SOS symbiotic organisms search SVC static VAR compensator TCPS thyristor-controlled phase shifter TCSC Thyristor-Controlled Series Compensator TLBO teaching-learning-based optimization

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Comparative performance assessment of several Fractional Order - Two Degree Freedom controllers tuned using GOA for LFC

Comparative performance assessment of several Fractional Ord...

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International Conference on Computational Intelligence and Data Science (ICCIDS)

作者： Tripathy, Debasis Choudhury, N. B. Dev Sahu, B. K. Natl Inst Techonol Silchar Elect Engn Dept Silchar 788010 India Siksha Anusandhan Univ Elect Engn Dept Bhubaneswar 751030 India

The work puts an earliest effort to compare the performances of different fractional order based feed-forward type two degree of freedom (2DOF-FO) controllers optimized with grasshopper optimization algorithm (GOA) for the study of load frequency control in interconnected system. As fractional order controllers (FOC) are gaining attention of researchers over integer order controller (IOC) in recent era, so the authors' main motive in this article is to highlight a comparative performance analysis between three different structures of feed-forward type 2DOF-FOC namely 2DOF-FO-proportional-integral-derivative (2DOF-FOPID), 2DOF-FO-integral-derivative (2DOF-FOID) and 2DOF-FOPI. These controllers are optimally designed through GOA technique. The 2DOF-FOPID controller ensured as the best one over other two controllers in GOA framework even in the presence of nonlinearities for a two area and three areas thermal system. Also, the robustness of the controllers is verified for two area system by applying different physical conditions like a random pattern of step load variations, changing the loading condition in area-1 and the system parameters within a range of 20%. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Fractional Order Controller grasshopper optimization algorithm Load Frequency Control Nonlinearities Two Degree of Freedom

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Optimal Allocation of Solar-Wind based DG Considering Uncertainty Using Improved grasshopper algorithm

Optimal Allocation of Solar-Wind based DG Considering Uncert...

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IEEE IFAC International Conference on Automation (ICA) / 24th Congress of the Chilean-Association-of-Automatic-Control (ACCA)

作者： Ramadan, Ashraf Ebeed, Mohamed Kamel, Salah Rahmann, Claudia Aswan Univ Dept Elect Engn Aswan Egypt Sohag Univ Dept Elect Engn Sohag Egypt Univ Chile Dept Elect Engn Santiago Chile

ISBN: (纸本)9781665401272

grasshopper optimization algorithm (GOA) is an efficient technique which mimics the movement orientation and lifestyle of grasshopper in natural. However, the GOA is applied for solve successfully numerous optimizations problem, it failed to solve other cases efficiently and it prone to stuck in local optima. Thus, an improved version of the GOA is proposed to enhance the performance the conventional GOA. The improved GOA (IGOA) is based on improving the exploration and the exploitation process of the GOA. The exploration is enhanced using a mutation operator to enable the algorithm to a new area to avoid the stagnation of this technique while the exploitation process is enhanced using an adaptive operator to update the positions of the grasshopper around the best so far solution. In this paper, the IGOA is applied to address the allocation problem of renewable energy resources in radial distribution grid (RDG). The renewable resources include hybrid solar wind based Distributed Generator (DG) units for the expected power loss minimization to consider the uncertainty in the electric system. The IGOA is tested on 85-bus distribution grid and the captured results are compared with the traditional GOA to verify its applicability and efficiency. Numerous scenarios are generated using Monte-Carlo simulation to take into consideration the uncertainties of system which include load demands, solar irradiation, and wind speed variations. The simulation results demonstrate that the IGOA is superior for addressing the allocation problem of DG compared with traditional GOA in terms of objective function.

关键词： grasshopper optimization algorithm DG renewable energy uncertainties

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Optimal Power Dispatch of Distributed Energy Resources in Unbalanced AC Microgrid Considering Load and Generation Uncertainties

Optimal Power Dispatch of Distributed Energy Resources in Un...

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International Conference on Decision Aid Sciences and Applications (DASA)

作者： Yousaf, Zeeshan Khan, Muhammad Faisal Nadeem Malik, Sarib Univ Engn & Technol Elect Engn Dept Taxila Pakistan

ISBN: (纸本)9781665495011

Distributed Energy Resource (DER) scheduling is usually performed while considering lumped load models in an unbalanced distribution system (UDS). However, it is not necessary that lumped load models represent the actual characteristics of the distribution system. Therefore, in this paper, DERs scheduling is performed while considering time-varying loading characteristics of each phase of UDS. grasshopper optimization algorithm (GOA) is used to optimize the performance of UDS. The proposed methodology is implemented on IEEE 37 node feeder, and its comparison with recent studies demonstrates the effectiveness of the approach.

关键词： AC Microgrid Distributed Energy Resources scheduling grasshopper optimization algorithm unbalanced distribution system

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Performance Enhancement of Distribution Systems using Distributed Generators and Capacitor Banks at Medium and Low Voltage Networks 20

Performance Enhancement of Distribution Systems using Distri...

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20th International Middle East Power Systems Conference (MEPCON)

作者： Elsayed, Abdullah M. Hegab, Mohamed M. Farrag, Sobhy M. Damietta Univ Fac Engn Dept Elect Engn New Damietta 34517 Egypt Menoufia Univ Fac Engn Dept Elect Engn Shibin Al Kawm 32511 Egypt

ISBN: (纸本)9781538666548

This paper introduces a comparison investigation between performance enhancement of distribution systems based on capacitor banks (CBs) or distributed generators (DGs) based-PVs. This investigation, also, consider the enhancement device placement on low voltage and medium voltage networks. The importance of this comparison is to determine the economic feasibility of the enhancement devices on the distribution systems at the low or medium voltage level and determine the degree of the distribution system's performance improvement for each case. This comparison has been applied on a real 22kV, 78-bus Egyptian distribution system. This system has nineteen 22/ 0.4 kV distribution transformers supplying a total lumped loads of 12.5MW and 7.7MVAr. grasshopper optimization algorithm "GOA", which represent one of the newest heuristic technique, has been applied in this paper. The results show that it is wrong to improve distribution systems depending only on improving MV networks but enhancing the low voltage networks may be more efficient and lower cost. Also, improper number or location selection of the connected enhancement devices have a negative impact on the performance of distribution systems.

关键词： Distribution systems Capacitor Banks Renewable Distributed Generators grasshopper optimization algorithm

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A scenario-based collaborative problem for a relief supply chain during post-disaster under uncertain parameters: a real case study in Dorud

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JOURNAL OF MODELLING IN MANAGEMENT 2023年第3期18卷 906-941页

作者： Bakhshi, Alireza Aghsami, Amir Rabbani, Masoud Univ Tehran Sch Ind & Syst Engn Coll Engn Ind Engn Tehran Iran Univ Tehran Sch Ind & Syst Engn Coll Engn Tehran Iran KN Toosi Univ Technol Sch Ind Engn Ind Engn Tehran Iran

Purpose Unfortunately, the occurrence of natural disasters is inevitable all over the world. Hence, this paper aims to analyze a scenario-based collaborative problem in a relief supply chain (RSC), where nongovernmental organizations can participate in relief activities with governmental organizations. This study focuses on location-allocation, inventory management and distribution planning under uncertain demand, budget, transportation and holding costs where government and private distribution centers receive relief items from suppliers then send them to affected areas. The performance of the proposed model is surveyed in a real case study in Dorud. Design/methodology/approach This paper develops a nonlinear mixed-integer programming model that seeks to maximize the coverage of demand points and minimize operating costs and traveled distance. The linear programming-metric technique and grasshopper optimization algorithm are applied to survey the model's applicability and efficiency. Findings This study compares noncollaborative and collaborative cases in terms of the number of applied distribution centers and RSC's goals, then demonstrates that the collaborative model not only improves the coverage of demand points but also minimizes cost and traveled distance. In fact, the presented approach helps governments efficiently surmount problems created after a disaster, notwithstanding existing uncertainties, by determining a strategic plan for collaboration with nongovernmental organizations for relief activities. Originality/value Relief strategies considered in previous research have not been sufficiently examined from the perspective of collaboration of governmental and nongovernmental organizations and provided an approach to develop the coverage of affected areas and reducing costs and traveled distance despite various uncertainties. Hence, the authors aim to manage RSCs better by offering a mathematical model whose performance has been proved in a real case stud

关键词： Relief supply chain management Collaborative model Distribution planning Location-allocation grasshopper optimization algorithm optimization

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