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Numerical reconstruction of turbid slab optical properties using global optimization algorithms

LASERS IN MEDICAL SCIENCE 2021年第1期36卷 43-54页

作者： Li, Xuesong Yang, Shangze Xiao, Di Wang, Shangning Shanghai Jiao Tong Univ Sch Mech Engn Natl Engn Lab Automot Elect Control Technol Shanghai 200240 Peoples R China

The detection and reconstruction of the optical properties within turbid slabs/plate parallel mediums have been widely investigated for its applications in medical diagnosis, atmosphere detection, etc., where the scattering of light would be expected. Although the scattering signal can be utilized for diagnostics purposes, the multiple scattering in the intermediate scattering regime (with an optical depth similar to 2-9) has posed a remarkable challenge. Existing optical tomography methods usually only reconstruct the reduced scattering coefficient to investigate the properties of the scattering target, while reconstruction efforts in analyzing the exact scattering phase function are rare. Solving such issues can provide much more information for proper interpretation of the characteristics of the turbid slab. This work demonstrates an inversion method based on optimization algorithms and the angular distribution of the transmitted light at the entrance plane and the exit plane of the sought medium. Candidate phase functions were pre-calculated and the optimization algorithm is able to reconstruct the phase function spatial distribution of the turbid slab with a satisfactory computational cost. Parametric studies were also performed to analyze the performance of each optimization algorithm used and the sensitivity of this Markov reconstruction scheme to noise.

关键词： Multiple scattering Markov chain Reconstruction optimization algorithm Turbid slab

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optimization of torsion and wear characteristics on reinforced steel wire rope

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2024年第2期52卷 752-772页

作者： Palanisamy, Saravana Kumar Krishnaswamy, Manonmani Govt Coll Technol Dept Mech Engn Coimbatore 641013 Tamil Nadu India Govt Coll Engn Dept Mech Engn Tirunelveli Tamil Nadu India

This work develops the optimization of torsional, wear, and fatigue life behaviors based on the hybrid emperor penguin social ski-driver for reinforced steel wire. Using granite and rutile particles reinforcement, steel wires are strengthened. Normal steel wire rope and reinforced wire rope are prepared with 7 strands and 15 wires. Using hybrid emperor penguin optimization-based social ski driver optimization (Hybrid EPSSD), the failure tests such as wear analysis, fatigue life, and torsional behavior are optimized. Besides, the performances of the experimented wire rope are predicted by using hybrid Elman recurrent Neural Network-based EPO (Hybrid ERNN-EPO). Using the Matlab 2018a platform, the optimization and prediction processes are performed. In this, reinforced wire ropes deliver enhanced performances for both experimented and optimization behaviors. From the results, the reinforced wire rope has the best performance which possesses less wear rate, more fatigue life, and torsion behavior are obtained. The experimental outcome of wear depth for reinforced wire rope is 0.18 mm and the optimized wear depth outcome from the Hybrid EPSSD approach is 0.16 mm. For reinforced wire rope, the optimized fatigue life is 4.50 x 10(4) times at 500 KN and the maximum fatigue life experimentally is 4.20 x 10(4) times. At a particular hoisting time, the optimization value for the location of maximum torsion angle is 243 to 18 mm is obtained and the experimental values are 240 to 15 m. The reinforced wire rope has a better performance compared to the steel wire rope.

关键词： Tower crane Reinforcement wire rope optimization algorithm Elman recurrent Neural Network

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On-the-Fly Unsteady Adjoint Aerodynamic and Aeroacoustic optimization Method

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AIAA JOURNAL 2024年第12期62卷 4779-4797页

作者： Zhi, Haolin Xiao, Tianhang Qin, Ning Deng, Shuanghou Lu, Zhaoyan Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing 210016 Peoples R China Univ Sheffield Dept Mech Engn Sheffield S1 3JD England Innovat Acad Microsatellites Shanghai 201304 Peoples R China State Key Lab High Temp Gas Dynam Beijing 100190 Peoples R China

An on-the-fly unsteady adjoint-based aerodynamic and aeroacoustic optimization methodology is presented, aiming to achieve practical engineering applications to explore high-efficiency and low-noise design for aerodynamic shapes. Firstly, a novel on-the-fly hybrid CFD-CAA approach is developed with a close integration of unsteady Reynolds-averaged Navier-Stokes equations and a fully viscous time-domain FW-H formulation. Subsequently, an adjoint-based sensitivity analysis method is proposed for unsteady aerodynamic and aeroacoustic problems with either stationary or moving boundaries, wherein a unified architecture for discrete-adjoint sensitivity analysis of both aerodynamics and aeroacoustics is achieved by integrating the on-the-fly hybrid CFD-CAA approach. The on-the-fly approach facilitates direct evaluation of partial derivatives required for solving adjoint equations, eliminating the need for explicitly preprocessing flow and adjoint variables at all time levels in a standalone adjoint CAA solver and consequently substantially reducing memory consumption. The proposed optimization methodology is implemented within an open-source suite SU2. Results show that the proposed on-the-fly adjoint methodology is capable of achieving highly accurate sensitivity derivatives while significantly reducing memory requirements by an order of magnitude, and further demonstrations of single-objective and coupled aerodynamic and aeroacoustic optimizations highlight the potential of the proposed method in exploring high-efficiency and low-noise design for aerodynamic shapes.

关键词： optimization algorithm Computational Fluid Dynamics Overall Sound Pressure Level Aerodynamic Shape optimization Aerodynamic Performance Discrete Adjoint Solvers Unsteady Aerodynamics Gradient-Based Design optimization Multidisciplinary Design optimization Computational Aeroacoustics

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Implementation of a New Solar-Powered Street Lighting System: optimization and Technical-Economic Analysis Using Artificial Intelligence

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IEEE ACCESS 2024年 12卷 46657-46667页

作者： Belloni, Elisa Massaccesi, Andrea Moscatiello, Cristina Martirano, Luigi Univ Perugia Dept Engn I-06132 Perugia Italy Sapienza Univ Rome Dept Elect Engn I-00185 Rome Italy

Public lighting system represents a key role in the energy transition process, considering the high electricity consumptions related to this sector. The integration of renewables could be suitable for this application and many solutions of solar-based lamps for street lighting are spreading. In this research work, a specific application of a PV-integrated lighting system was installed in the center of Italy along a footpath and monitored for several months, both in terms of electricity parameters and lighting behavior. It is equipped with monocrystalline photovoltaic cells, a lithium-based battery, and a LED lamp. The measured data allow the development of an optimization algorithm in Python program for the correct management of the solar streetlamp and the forecasting of electricity consumptions for different boundary conditions (e.g. Italian geographical position). The energy taken from grid in a year is very low with respect to a traditional lamp not powered by renewables: only 43-46% in central-northern regions and 35% in south region. Data are also used to study the possible substitution of all the traditional lamppost of the walkway with the novel proposed system. A technical-economic analysis is carried out to analyze the effectiveness of this solution not only in terms of electricity consumptions reduction, but also costs savings. The suitability of the investment and the payback time depend on Italian national price of electricity and initial cost of the solar lamp. The results can be applied to similar case studies in Italy to save electricity consumptions and reduce CO2 emissions.

关键词： Lighting Batteries Electricity Green products Voltage control Renewable energy sources Pedestrians Artificial intelligence Energy consumption optimization methods Photovoltaic systems Energy storage Technology management Economics electricity consumptions LED optimization algorithm photovoltaic (PV) storage streetlamp technical-economic analysis

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Smart cities optimization using computational intelligence in power-constrained IoT sensor networks

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Darabkh, Khalid A. Al-Akhras, Muna Univ Jordan Sch Engn Dept Comp Engn Amman 11942 Jordan

This paper introduces the Innovative Clustering Energy Efficient Equilibrium Optimizer-based Multi-Hop Routing Protocol (ICEE-EO-MHRP) for addressing the energy constraint in Internet of Things (IoT) network clustering utilizing the Equilibrium Optimizer (EO), a yet efficient computational intelligence method that is used for selecting Designated Cluster Head (DCH) and Backup DCH (BDCH). Additionally, ICEE-EO-MHRP deals with the IoT energy problem by incorporating a novel cost function that ends up of selecting Designated Relays (DRs) and backup DRs for the purpose of forwarding the traffic towards the sink node. Our protocol substantially reduces messages' exchanges between IoT Sensor Nodes (SNs) by making the replacement of DCH and BDCH dependent on their energy levels dropping below a threshold. To ensure a balanced communication load and efficient scheduling, an innovative deterministic distributed-time division multiple access system is employed. Not only to this extent, but we address data redundancy issue, raised among those quite adjacent SNs, and accordingly propose an efficient management that guarantees having a coherent protocol. In addition to that, device and link failures are professionally addressed by suggesting recovery mechanisms that optimize the proposed protocol. Dealing with these impairments puts our approach well ahead of the competition since it addresses the most practical issues and scenarios, particularly those with challenging environmental constraints. The simulation results demonstrate primarily that our protocol significantly improves the network lifetime by 157.83 % and 109.81 % in comparison to Particle Swarm optimization and Tabu Search (Tabu-PSO) and Energy-Efficient CH Selection by Improved Sparrow Search algorithm utilizing Differential Evolution (EECHS-ISSADE), respectively. Comparing ICEE-EO-MHRP to Tabu-PSO and EECHS-ISSADE reveals improvements in residual energy of 335.87 % and 230.05 %, respectively. Furthermore, in compar

关键词： IoT Clustering optimization algorithm EO Routing network lifetime Efficient energy Load balancing Average delay

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Fuzzy Boosted Sooty Tern optimization (FBSTO): Fault Detection and Node Recovery Approach for WSN

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第1期38卷

作者： Chakraborty, Sujay Raghuvanshi, Ajay Singh Natl Inst Technol Dept Elect & Commun Engn Raipur Chhattisgarh India

The fault node detection and recovery is essential in WSN for improving the network lifetime and node connectivity. Ensuring the tradeoff between energy consumption and node recovery is challenged due to redundant nodes. Hence, a novel approach is developed based on the objective of automatic recovery from failure, redundant node elimination, fault node replacement, and minimalizing energy consumption. In this approach, the fuzzy boosted sooty tern optimization (FBSTO) technique is proposed for fault node detection and replacement. Based on the node density and distance, the nodes are clustered. Then, the cluster head selection process can be accomplished with a fuzzy logic approach based on distance calculation, energy consumption, and Quality of service (QoS) nodes. The node replacement is carried out through cascaded movement, which minimizes energy utilization. The efficiency of the FBSTO approach is estimated with packet delivery ratio, packet loss ratio, end-to-end delay, and energy consumption. The proposed approach reduces the end-to-end delay to 10 ms for random deployment of 100 nodes. Also, the packet delivery ratio performance of the proposed approach is 143 for 100 SNs. For existing Multi-objective Cluster Head Based Energy-aware Optimized Routing (MCH-EOR), Ant Lion optimization (ALO), Particle swarm optimization (PSO), Gray Wolf optimization (GWO), and Genetic approach (GA), the packet delivery ratio is reduced to 130, 60, 50, and 100. Compared with the existing approaches, the proposed approach provides better performance.

关键词： cascaded movement cluster head selection clustering fault node detection fuzzy logic node replacement optimization algorithm redundant node

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Layout optimization algorithms for the Offshore Wind Farm with Different Densities Using a Full-Field Wake Model

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ENERGIES 2023年第16期16卷 5916页

作者： Liang, Zhichang Liu, Haixiao Tianjin Univ State Key Lab Hydraul Engn Simulat & Safety Tianjin 300072 Peoples R China

To decrease the power deficit of a wind farm caused by wake effects, the layout optimization is a feasible way for the wind farm design stage. A suitable optimization algorithm can significantly improve the quality and efficiency of the optimization process. For exploring the high-performance algorithms under different layout densities, a comparison is conducted by optimizing the layout of a real offshore wind farm with five algorithms, namely two population-based algorithms and three single-point algorithms. Wake effects are considered by a full-field wake model. A penalty function is proposed for the population-based algorithms to handle the constraint violations. Different iterations and constraints of the layout density are applied in the optimization. The random search has the best optimization results in all the cases and the control of the feasibility check is necessary for this algorithm. More iterations can advance the optimization results. The density constraint greatly affects the computational cost of the random search, which is significantly increased under the strict constraint. Except under the strict constraint, the random search has the best performance of optimization efficiency. A combination of the pattern search and random search is recommended when the strict constraint is applied in the layout optimization.

关键词： wind farm layout optimization optimization algorithm layout density wake model penalty function random search pattern search

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optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartD期255卷

作者： Kazemi, Farzin Asgarkhani, Neda Jankowski, Robert Gdansk Univ Technol Fac Civil & Environm Engn ul Narutowicza 11-12 PL-80233 Gdansk Poland Univ Naples Federico II Sch Polytech & Basic Sci Dept Struct Engn & Architecture Naples Italy

Efficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and postprocessing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced concrete shear walls (RCSWs) by introducing stacked machine learning (Stacked ML) models based on Bayesian optimization (BO), genetic algorithm (GA), particle swarm optimization (PSO), and gradient-based optimization (GBO) algorithms. The study investigates 4-, to 15-Story RCSWs assuming different bay lengths and soil types to build a comprehensive database based on the incremental dynamic analysis (IDA) subjected to 56 near-field pulse-like and no-pulse records. Having 227,200 and 63,384 data points for a median of IDA curve (MIDA) and seismic probability curve, respectively, the proposed Stacked ML models have shown good performance on curve fitting ability by accuracy of 99.1% and 99.4% for MIDA and seismic fragility curves, respectively. In addition, the proposed models can estimate the mean annual frequency, lambda, which is a key parameter in seismic risk assessment of buildings. To provide the results of the study for general buildings, a user-friendly GUI is proposed that facilitates result utilization, offering insights into seismic performance levels, providing the estimated MIDA and seismic failure probability curves, and mean annual frequency calculations for specific performance levels and seismic hazard curves.

关键词： Reinforced concrete shear wall Machine learning algorithm Seismic failure probability optimization algorithm Seismic performance curve Seismic retrofit

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optimization of terminal area arrival flight sorting based on an improved sparrow search algorithm

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SCIENCE PROGRESS 2024年第1期107卷 368504241238078页

作者： Zhao, Weixi Liang, Te Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming Peoples R China Beihang Univ Fac Instrumentat Sci & Optoelect Engn Beijing Peoples R China Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Peoples R China

At present, airspace congestion and flight delays have become widespread concerns. This study aims to optimize the sequencing of arrival flights in the terminal area of multirunway airports. Considering the constraints of multiple runways, slant intervals and moving flight positions, this article establishes an optimization model for arrival flight sequencing in a multirunway airport terminal area. Accordingly, an improved sparrow search algorithm (ISSA) is proposed based on Chebyshev chaotic mapping, the golden sine strategy, and the variable neighborhood strategy. Through six basic test functions, the ISSA is compared with particle swarm optimization, the whale optimization algorithm, the genetic algorithm, and other algorithms to verify its superiority. Finally, two sets of instance data from Kunming Changshui Airport were used for experiments. The results show that the total delay times (TDTs) of small-scale flights (number of aircraft: 29) and large-scale flights (number of aircraft: 147) are 55.3% and 20.5% lower, respectively, than those of the first-come-first-served algorithm. The superiority of the ISSA designed in this article is verified, and it can significantly reduce the TDTs of arrival flights. It is suitable for optimizing arrival flights during peak hours at most airports. This approach provides theoretical support for optimizing the sorting of flights in terminal areas.

关键词： Flight sequence optimization algorithm sparrow algorithm air traffic management

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optimization problems in water distribution systems using Non-dominated Sorting Genetic algorithm II: An overview

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AIN SHAMS ENGINEERING JOURNAL 2023年第4期14卷

作者： Shirajuddin, Talhah Mohamad Muhammad, Nur Shazwani Abdullah, Jazuri Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Civil Engn Bangi 43600 Selangor Malaysia Univ Teknol MARA Coll Engn Sch Civil Engn Shah Alam 40450 Selangor Malaysia

The application of a genetic algorithm inspired multi-objectives optimization algorithms, known as the Non-dominated Sorting Genetic algorithm (NSGAii) in solving optimization problems related to Water Distribution System (WDS) is critically reviewed. The list of variations of NSGAii used by other research-ers in solving WDS are assessed, including an improved version of NSGAii and its combination with other algorithms in solving the problems. The optimization problems include WDS optimal design, pipe reha-bilitation strategies, water quality, leakages and pump operation. The existing algorithm has been improved to solve multi-objectives and yet researchers are keen in using the NSGAii algorithm. Improvised and revolutionary algorithms, such as RNSGAii, sigma-NSGAii and NSGAiii, still received less attention even though these algorithms have been introduced since 2005 and model capabilities to solve more complex problem is proved. The application of these algorithms should be seriously considered due to increasing aging of pipe in the future.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://***/licenses/ by-nc-nd/4.0/).

关键词： NSGAii optimization algorithm Optimal design problems Pipe rehabilitation Water distribution system Water quality problems

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