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A hybrid Grasshopper optimization algorithm and Harris Hawks Optimizer for Combined Heat and Power Economic Dispatch problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期111卷 104753-104753页

作者： Ramachandran, Murugan Mirjalili, Seyedali Nazari-Heris, Morteza Parvathysankar, Deiva Sundari Sundaram, Arunachalam Gnanakkan, Christober Asir Rajan Charles KCG Coll Technol Dept Elect & Elect Engn Chennai 600097 India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Penn State Univ Dept Architectural Engn State Coll PA 16803 USA Pondicherry Engn Coll Dept Elect & Elect Engn Pondicherry 605014 India Jubail Ind Coll Dept Elect & Elect Engn Technol Jubail Ind City 31961 Saudi Arabia

The Combined Heat and Power Economic Dispatch (CHPED) is a real-world optimization problem with several complex constraints that has been a topic of studies around energy systems and optimization processes. This paper attempts to conceptualize a potent algorithm by combining the Modified Grasshopper optimization algorithm (MGOA) and the Improved Harris Hawks Optimizer (IHHO) for attaining a better balance between the beginning stages of global search and the latter stages of global convergence. The proposed attempt is abbreviated as MGOA-IHHO. Firstly, the chaotic and Opposition-Based Learning (OBL) methods are invoked to generate the initial population. Second, the mathematical model of the conventional Grasshopper optimization algorithm (GOA) is modified using Sine-Cosine Acceleration Coefficients (SCAC) to simulate the global exploration at the initial iterations and graduating to the global convergence at the final stages of optimization. Hence, it is named MGOA. Finally, the adaptive search mechanism integrates the two improved search phases of HHO with a search phase of MGOA to improve the performance of the proposed optimization method. This mechanism investigates the best solution for the aging level of the individual during the optimal evaluation process for choosing an appropriate search phase in MGOA-IHHO. The intended effect of the proposed MGOA-IHHO method is verified with other nature-inspired methods on standard single-objective test functions including 23 benchmark problems, 30 test suits of IEEE Congress on Evolutionary Computation 2017 (CEC2017), and four CHPED problems. The statistical results ascertain that the proposed hybridized MGOA-IHHO is capable of providing promising results when compared with its variants and optimization algorithms introduced in the literature.

关键词： Combined Heat and Power Economic Dispatch Grasshopper optimization method Sine-Cosine Acceleration Coefficients Harris Hawks Optimizer MGOA-IHHO Benchmark problem optimization algorithm

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A novel UAV path planning approach: Heuristic crossing search and rescue optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Zhang, Chaoqun Zhou, Wenjuan Qin, Weidong Tang, Weidong Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Unmanned aerial vehicle (UAV) path planning plays an important role in the flight process of an UAV, which needs an effective algorithm to deal with UAV path planning problem. The search and rescue optimization algorithm (SAR) is easy to implement and has the characteristics of flexible, but it has slow convergence speed and has not been applied to UAV path planning. To address these problems, a heuristic crossing search and rescue optimization algorithm (HC-SAR) is proposed. HC-SAR combines a heuristic crossover strategy with the basic SAR to improve the convergence speed and maintain the population diversity in the optimization process. Furthermore, a real-time path adjustment strategy is proposed to straighten the UAV flight path. In addition, cubic B-spline interpolation is used to smooth the generated path. Comprehensive experiments including two-dimensional and three-dimensional environments for different threat zone are conducted to validate the performance of HC-SAR. The results show that HC-SAR has a high convergence speed and can successfully obtain a safe and efficient path, and it significantly outperforms SAR, differential evolution (DE), ant lion optimizer (ALO), squirrel search algorithm (SSA) and salp swarm algorithm (SSA) in all the cases. These results suggest that the proposed algorithm can effectively serve as an alternative for solving UAV path planning problem.

关键词： Path planning Heuristic crossing search and rescue optimization algorithm Search and rescue optimization algorithm Path adjustment Unmanned aerial vehicle

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Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two optimization algorithm Models

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BUILDINGS 2023年第5期13卷 1242页

作者： Ren, Jiajun Sun, Xianbin Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan 430068 Peoples R China

The determination of the bearing capacity of pile foundations is very important for their design. Due to the high uncertainty of various factors between the pile and the soil, many methods for predicting the ultimate bearing capacity of pile foundations focus on correlation with field tests. In recent years, artificial neural networks (ANN) have been successfully applied to various types of complex issues in geotechnical engineering, among which the back-propagation (BP) method is a relatively mature and widely used algorithm. However, it has inevitable shortcomings, resulting in large prediction errors and other issues. Based on this situation, this study was designed to accomplish two tasks: firstly, using the genetic algorithm (GA) and particle swarm optimization (PSO) to optimize the BP network. On this basis, the two optimization algorithms were improved to enhance the performance of the two optimization algorithms. Then, an adaptive genetic algorithm (AGA) and adaptive particle swarm optimization (APSO) were used to optimize a BP neural network to predict the ultimate bearing capacity of the pile foundation. Secondly, to test the performance of the two optimization models, the predicted results were compared and analyzed in relation to the traditional BP model and other network models of the same type in the literature based on the three most common statistical indicators. The models were evaluated using three common evaluation metrics, namely the coefficient of determination (R-2), value account for (VAF), and the root mean square error (RMSE), and the evaluation metrics for the test set were obtained as AGA-BP (0.9772, 97.8348, 0.0436) and APSO-BP (0.9854, 98.4732, 0.0332). The results show that compared with the predicted results of the BP model and other models, the test set of the AGA-BP model and APSO-BP model achieved higher accuracy, and the APSO-BP model achieved higher accuracy and reliability, which provides a new method for the prediction of the ul

关键词： pile foundation ultimate bearing capacity AGA-BP APSO-BP optimization algorithm

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A quantum online portfolio optimization algorithm

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QUANTUM INFORMATION PROCESSING 2024年第3期23卷 63-63页

作者： Lim, Debbie Rebentrost, Patrick Univ Latvia Riga Latvia Ctr Quantum Technol Singapore Singapore

Portfolio optimization plays a central role in finance to obtain optimal portfolio allocations that aim to achieve certain investment goals. Portfolio optimization also provides a rich area to study the application of quantum computers to obtain advantages over classical computers. In a multi-period setting, we give a sampling version of an existing classical online portfolio optimization algorithm by Helmbold et al., for which we in turn develop a quantum version. The quantum advantage is achieved by using techniques such as quantum state preparation, inner product estimation and multi-sampling. Our quantum algorithm provides a quadratic speedup in the time complexity, in terms of n, where n is the number of assets in the portfolio. The transaction cost of both of our classical and quantum algorithms is independent of n which is especially useful for practical applications with a large number of assets.

关键词： Online algorithm optimization algorithm Portfolio optimization Quantum algorithm Quantum computing

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Flood mapping based on novel ensemble modeling involving the deep learning, Harris Hawk optimization algorithm and stacking based machine learning

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APPLIED WATER SCIENCE 2024年第4期14卷 78页

作者： Costache, Romulus Pal, Subodh Chandra Pande, Chaitanya B. Islam, Abu Reza Md. Towfiqul Alshehri, Fahad Abdo, Hazem Ghassan Transilvania Univ Brasov Dept Civil Engn 5 Turnului Str Brasov 500152 Romania Danube Delta Natl Inst Res & Dev 165 Babadag St Tulcea 820112 Romania Natl Inst Hydrol & Water Management Bucuresti Ploiesti Rd 97E Bucharest 013686 Romania Univ Bucharest Res Inst 90-92 Sos Panduri Bucharest 050107 Romania Univ Burdwan Dept Geog Bardhaman 713104 West Bengal India Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Thi Qar 64001 Iraq Univ Tenaga Nas Inst Energy Infrastructure Kajang 43000 Malaysia King Saud Univ Coll Sci Geol & Geophys Dept Abdullah Alrushaid Chair Earth Sci Remote Sensing Riyadh Saudi Arabia Begum Rokeya Univ Dept Disaster Management Rangpur 5400 Bangladesh Daffodil Int Univ Dept Dev Studies Dhaka 1216 Bangladesh Tartous Univ Fac Arts & Humanities Geog Dept Tartous Syria

Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buzau river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and machine learning benchmarks in order to prepare flood potential maps at the basin scale. In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk optimization-Index of Entropy (Stacking-HHO-IOE). The flood sample was divided into training (70%) and validating (30%) sample, meanwhile the prediction ability of flood conditioning factors was tested through the Correlation-based Feature Selection method. ROC Curve and statistical metrics were involved in the results validation. The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance approximate to 20%), distance from river (importance approximate to 17.5%), land use (importance approximate to 12%) and TPI (importance approximate to 10%). The importance values were used to compute the flood susceptibility, while Natural Breaks method was used to classify the results. The high and very high flood susceptibility is spread on approximately 35-40% of the study zone. The ROC Curve, in terms of Success, Rate shows that the highest performance was achieved FPIDLNN-HHO-IOE (AUC = 0.97), followed by FPIStacking-HHO-IOE (AUC = 0.966) and FPIMLP-HHO-IOE (AUC = 0.953), while the Prediction Rate indicates the FPIStacking-HHO-IOE as being the most performant model with an AUC of 0.977, followed by FPIDLNN-HHO-IOE (AUC = 0.97) and FPIMLP-HHO-IOE (AUC = 0.924).

关键词： Artificial intelligence optimization algorithm Flood potential Buzau river basin Romania

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Multi-strategy improved sand cat optimization algorithm-based workflow scheduling mechanism for heterogeneous edge computing environment

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 43卷

作者： Jayalakshmi, P. Ramesh, S. S. Subashka SRM Inst Sci & Technol Ramapuram Dept Comp Sci & Engn Chennai Tamil Nadu India

Edge computing is one of the predominant technologies which facilitates the option of bringing out the computing resources closer to the location of the end users when they are utilized by them. This facility offered by edge computing technology need to reduce the utilization of network bandwidth and response time with respect to the user's workflow. In this paper, Multi-Strategy Improved Sand Cat Swarm Optimisation algorithm (MSISCSOA)-based workflow scheduling mechanism is proposed for handling the challenges of workflow scheduling in cloud-edge computing environment. The core objective of this MSISCSOA-based workflow scheduling algorithm targets on minimizing the execution latency and energy consumption to facilitate timely and on-demand end users' satisfaction of resources. This MSISCSOA scheme is adopted with the improvement introduced using random variation and elite collaborative strategies, such that well-balanced the trade-off between exploration and exploitation is achieved. This improvement is introduced over Sand Cat optimization algorithm (SCOA) using the merits of dynamic random search and joint opposite selection strategies that accelerates the convergence of the algorithm with increased global optimization and searching efficiency. It specifically improved SCOA using random variation for escaping from the local point of optimality. It also used well distributed pareto fronts and population evolution multi-strategy that aids in searching solutions with maximized diversity. The simulation experiments conducted using the datasets of Montage, Cybershake, LIGO and SIPHT an average confirmed minimized execution latency of 21.38 % and energy consumptions of 19.56 %, better than the baseline Ant Colony optimization algorithm-Based Workflow Scheduling (IACOAWS), Quadratic Penalty Function-based Particle Swarm optimization algorithm (QPF-PSOA), Biogeography optimization (BBO) algorithm based Multi-Objective Task Scheduling (BBOAMOTS) and Different Evolution-ba

关键词： Cloud-edge computing Workflow scheduling Multi-strategies improved sand cat optimization algorithm Execution latency Energy consumptions Pareto optimal solutions

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The Compressive Strength Prediction for FRP-Confined Concrete in Circular Columns by Applying the Normalized AlexNet-ELM and the Advanced Red Fox optimization algorithm

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ADVANCED THEORY AND SIMULATIONS 2022年第4期5卷

作者： Cui, Wenming Zhao, Li-Cai Xu, Yi-Peng Mamlooki, Mina Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Shandong Peoples R China Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn 43Keelung RdSec 4 Taipei 106 Taiwan China Railway 19th Bur Grp Third Engn Co Ltd Shenyang 110136 Peoples R China Tiangong Univ Sch Math Sci Tianjin 300387 Peoples R China Univ Mohaghegh Ardabili Ardebil Iran

Fiber Reinforced Polymers (FRP) can be widely utilized in civil engineering because of their helpful features like high corrosion resistance for aggressive surroundings and high strength to weight ratio. By providing a lateral confining pressure, the concrete compressive strength is increased. Giving an analytic model that can forecast the FRP strength is the purpose of this study. This model is according to the normalized AlexNet Extreme Learning Machine and the Advanced Red Fox optimization algorithm (AlexNet-ELM-ARFO). The AlexNet-ELM-ARFO algorithm's innovation includes a formulation of an analytic relation, which doesn't attend to the established effectiveness parameter that is in the models introduced in the literature. An extended empirical dataset is utilized for defining the suggested formulation's variables. The suggested model is applied to circular columns including continuous FRP. The predictions' validation is shown by parametric research and the precision is examined by an empirical against theoretic comparison. A supplementary comparison is demonstrated with consideration of the theoretic prediction gained from the given method and the formulations' results using significant design codes. Outcomes prove that the used method is adapted to the FRP-confined concrete design and ensures an improved precision in comparison with respect to the available competitors.

关键词： fiber-reinforced polymers optimization algorithm red fox optimization reinforced concrete

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Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-based optimization algorithm

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CHEMOSPHERE 2021年 282卷 131012-131012页

作者： Bui Hoang Bac Hoang Nguyen Nguyen Thi Thanh Thao Le Thi Duyen Vo Thi Hanh Nguyen Tien Dung Luong Quang Khang Do Manh An Hanoi Univ Min & Geol Fac Geosci & Geoengn Dept Explorat Geol Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Ctr Excellence Anal & Expt Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Min Fac Dept Surface Min Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Ctr Min Electromech Res Duc Thang Ward 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Fac Basic Sci Dept Chem Duc Thang Ward 18 Vien St Hanoi Vietnam

The efforts of this study aimed to evaluate the feasibility of the nanotubular halloysites in weathered pegmatites (NaHWP) for removing heavy metals (i.e., Cd2+, Pb2+) from water. Furthermore, two novel intelligent models, such as teaching-learning-based optimization (TLBO)-artificial neural network (ANN), and TLBO-support vector regression (SVR), named as TLBO-ANN and TLBO-SVR models, respectively, were proposed to predict the Cd2+ and Pb2+ absorption efficiencies from water using the NaHWP absorbent. Databases used, including 53 experiments for Pb2+ absorption and 56 experiments for Cd2+ absorption from water, under the catalysis of different conditions, such as initial concentration of Pb2+ and Cd2+, solution pH, adsorbent weight, and contact time. Subsequently, the TLBO-ANN and TLBO-SVR models were developed and applied to predict the efficiencies of Cd2+ and Pb2+ absorption from water, aiming to evaluate the role as well as the effects of different conditions on the absorption efficiencies using the NaHWP absorbent. The standalone ANN and SVM models were also taken into consideration and compared with the proposed hybrid models (i.e., TLBO-ANN and TLBO-SVR). The results showed that the NaHWP detected in a Kaolin mine (Vietnam) with 70% nanotubular halloysites is a potential adsorbent for water treatment to eliminate heavy metals from water. The two novel hybrid models proposed, i.e., TLBO-ANN and TLBO-SVR, also yielded the dominant performances and accuracies in predicting the Cd2+ and Pb2+ absorption efficiencies from water, i.e., RMSE = 1.190 and 1.102, R-2 = 0.951 and 0.957, VAF = 94.436 and 95.028 for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Pb2+ absorption efficiency from water;RMSE = 3.084 and 3.442, R-2 = 0.971 and 0.965, VAF = 96.499 and 96.415 for the TLBO-ANN and TLBOSVR models, respectively, in predicting the Cd2+ absorption efficiency from water. Furthermore, the validation results also demonstrated these findings in practic

关键词： Heavy metals Water treatment Nanotubular halloysites Weathered pegmatites Artificial intelligence optimization algorithm

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Characterization of polyvinyl alcohol hydrogels as tissue-engineered cartilage scaffolds using a coupled finite element-optimization algorithm

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JOURNAL OF BIOMECHANICS 2020年第0期99卷 109525-000页

作者： Nazouri, Mohadeseh Seifzadeh, Alireza Masaeli, Elahe Islamic Azad Univ Najafabad Branch Dept Med Engn Najafabad Iran ACECR Royan Inst Biotechnol Cell Sci Res Ctr Dept Cellular Biotechnol Esfahan Iran Islamic Azad Univ Khomeinishahr Branch Dept Med Engn Khomeinishahr Iran

Mechanical strength along with high biocompatibility and water absorbing are among main characteristics of a desirable scaffold for cartilage tissue engineering. Having these properties, polyvinyl alcohol (PVA) can be a good option for constructing cartilage tissue engineering scaffolds. In this study, PVA hydrogel was produced by freeze-thaw crosslinking method, and its mechanical properties such as viscoelastic and hyperplastic behavior, which cannot be obtained analytically, was investigated with a coupled finite element (FE)-optimization algorithm and stress relaxation experimental data. To obtain isotropic hyper-viscoelastic constitutive parameters of PVA scaffolds, the Mooney-Rivlin and Neo-Hooke strain energy functions, in which shear and bulk moduli varies with time, were applied. Results showed that predicted mechanical responses of scaffolds by the Mooney-Rivlin model better fitted stress-relaxation experiments than those obtained by Neo-Hooke one. Also, the properties obtained from the finite element model, such as the bulk and the shear moduli, showed that, after successful in vitro and in vivo experiments, PVA hydrogel may be introduced as a cartilage substitute for future tissue engineering therapies. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Cartilage Polyvinyl alcohol optimization algorithm Finite Element

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Sensitivity analysis and optimization algorithm-based on nonlinear programming

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2008年第9期E91A卷 2426-2434页

作者： Oda, Masayoshi Yamagami, Yoshihiro Kawata, Junji Nishio, Yoshifumi Ushida, Akio Univ Tokushima Fac Engn Dept Elect & Elect Engn Tokushima 7708506 Japan Tokushima Bunri Univ Fac Engn Dept Mech & Elect Engn Sanuki 7692193 Japan

We propose here a fully Spice-oriented design algorithm of op-amps for attaining the maximum gains under low power consumptions and assigned slew-rates. Our optimization algorithm is based on a well-known steepest descent method combining with nonlinear programming. The algorithm is realized by equivalent RC circuits with ABMs (analog behavior models) of Spice. The gradient direction is decided by the analysis of sensitivity circuits. The optimum parameters can be found at the equilibrium point in the transient response of the RC circuit. Although the optimization time is much faster than the other design tools, the results might be rough because of the simple transistor models. If much better parameter values are required, they can be improved with Spice simulator and/or other tools.

关键词： sensitivity analysis steepest descent method optimization algorithm spice-oriented

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