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Optimization of cables size and prestressing force for a single pylon cable-stayed bridge with jaya algorithm

STEEL AND COMPOSITE STRUCTURES 2020年第6期34卷 853-862页

作者： Atmaca, Barbaros Dede, Tayfun Grzywinski, Maksym Karadeniz Tech Univ Dept Civil Engn Trabzon Turkey Czestochowa Tech Univ Fac Civil Engn Czestochowa Poland

In recent years, due to the many advantages cable-stayed bridges have often constructed in medium and long span. These advantages can be listed as an aesthetically pleasing appearance, economic and easy construction, etc. The main structural elements of cable-stayed bridges are listed as deck, pylon, cables and foundation. Perhaps one of the most vital and expensive of these structural elements is stay-cables. Stay-cables ensure the allowable displacement and distribution of bending moments along the bridge deck with prestressing force. Therefore the optimum design of the stay-cables and prestressing force are very important in achieving the performance expected from the cable-stayed bridges. This paper aims to obtain the stay-cables size and prestressing force optimization of the cable-stayed bridge. For this purpose, single pylon and fan type cable configuration Manavgat Cable-Stayed Bridge was selected as an example. The three dimensional (3D) finite element model (FEM) of the bridge was created with SAP2000. Analysis of the 3D FEM of the bridge was conducted under the different combined effects of the self-weight of the structural element, prestressing force of stay-cable and live load. Stay-cable stress and deck displacement were taken into account as constraints for the optimization problem. To optimize this existing bridge a metaheuristic algorithm named jaya was used in the optimization process. 3D FEM of the selected bridge was repeatedly analyzed by using Open Applicable Programming Interface (OAPI) properties of SAP2000. To carry out the optimization process the developed program which integrates the jaya algorithm and the required codes for calling SAP2000 is coded in MATLAB. At the end of the study, the total weight of the stay-cables was reduced more than 40% according to existing stay cables under loads taken into account.

关键词： SAP2000-OAPI size optimization prestressing force cable-stayed bridge jaya algorithm

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Allocation of DG in Distribution Network Using Parameter Independent jaya algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第3期18卷 1675-1692页

作者： Pandey, Anand Kumar Kirmani, Sheeraz Kumar, Ankesh Akhtar, Iram JSS Acad Techinal Educ Elect & Elect Engn Noida India Aligarh Muslim Univ Elect Engn Aligarh India Jamia Millia Islamia Elect Engn New Delhi India

Integration of DGs in the distribution network has led to challenges in making the system reliability, and other parameters improve. This paper gives a detailed discussion on the placement of DG in the distribution system using the new algorithm 'jaya' having no control parameter which makes the algorithm simple than previous used algorithms and also give best solutions according to the study. Three system parameters are considered for DG placement solution: active power loss reduction, voltage deviation reduction and voltage stability index maximization. The methodology is presented to calculate the optimal values for varying load profiles i.e. constant, residential, commercial and industrial. Type-1 DG placement is considered for placement. Simultaneous improvement of all factors using weight based multi-objective function is used. Standard test system IEEE-33 and 69 bus is used to validate the applicability of the methodology and compared to other optimization methods, the results presented proves the jaya to be better optimization of optimal DG calculation as the method is simple, feasible and have good applicability with other nature inspired and meta-heuristic methods.

关键词： jaya algorithm Distributed generation Power system optimization Distribution system

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Structural damage identification with output-only measurements using modified jaya algorithm and Tikhonov regularization method

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SMART STRUCTURES AND SYSTEMS 2023年第3期31卷 229-245页

作者： Zhang, Guangcai Wan, Chunfeng Xie, Liyu Xue, Songtao Southeast Univ Key Lab concrete & prestressed concrete Struct Minist Educ Nanjing Peoples R China Tongji Univ Res Inst Struct Engn & Disaster Reduct Coll Civil Engn Shanghai Peoples R China Tohoku Inst Technol Dept Architecture Sendai Japan

The absence of excitation measurements may pose a big challenge in the application of structural damage identification owing to the fact that substantial effort is needed to reconstruct or identify unknown input force. To address this issue, in this paper, an iterative strategy, a synergy of Tikhonov regularization method for force identification and modified jaya algorithm (M-jaya) for stiffness parameter identification, is developed for damage identification with partial output-only responses. On the one hand, the probabilistic clustering learning technique and nonlinear updating equation are introduced to improve the performance of standard jaya algorithm. On the other hand, to deal with the difficulty of selection the appropriate regularization parameters in traditional Tikhonov regularization, an improved L-curve method based on B-spline interpolation function is presented. The applicability and effectiveness of the iterative strategy for simultaneous identification of structural damages and unknown input excitation is validated by numerical simulation on a 21-bar truss structure subjected to ambient excitation under noise free and contaminated measurements cases, as well as a series of experimental tests on a five-floor steel frame structure excited by sinusoidal force. The results from these numerical and experimental studies demonstrate that the proposed identification strategy can accurately and effectively identify damage locations and extents without the requirement of force measurements. The proposed M-jaya algorithm provides more satisfactory performance than genetic algorithm, Gaussian bare-bones artificial bee colony and jaya algorithm.

关键词： B-spline interpolation function damage identification force identification jaya algorithm k-means clustering Tikhonov regularization

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Multi-objective collaborative optimization in cement calcination process: A time domain rolling optimization method based on jaya algorithm

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JOURNAL OF PROCESS CONTROL 2021年 105卷 117-128页

作者： Hao, Xiaochen Gao, Yong Yang, Xunian Wang, Junwei Yanshan Univ Sch Elect Engn Qinhuangdao 066000 Hebei Peoples R China

Coal consumption and free calcium oxide (f-CaO) content are two important production indicators in the cement calcination process, and its collaborative optimization is of great significance to improve the production performance. However, due to the multiple dynamic interferences and multiple conflicts between coal consumption and f-CaO, product quality is unstable and coal consumption becomes high. To address the problems, this paper proposes a multi-objective collaborative optimization method for cement calcination process. This method takes the coal consumption and f-CaO content as the optimization objectives to establish a multi-objective optimization model. In addition, to solve the problem that static single-step optimization is difficult to track the continuous change of working conditions, we propose a time domain rolling multi-objective jaya algorithm (TDRM-jaya) to optimize the model. The algorithm, involving a population update strategy, a selection strategy and a time domain rolling strategy, realizes the dynamic rolling optimization for the cement calcination process. The test comparison with several common intelligent optimization algorithms demonstrates the advantages of using TDRM-jaya. Besides, practical industrial data validates the effectiveness of the proposed optimization method. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Cement calcination process Coal consumption Free calcium oxide Multi-objective optimization jaya algorithm

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Optimum discrete balancing of the threshing drum using jaya algorithm

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2022年第1期50卷 100-114页

作者： Singh, Prem Chaudhary, Himanshu Swami Keshvanand Inst Technol Management & Gramot Dept Mech Engn Jaipur Rajasthan India Malaviya Natl Inst Technol Dept Mech Engn Jaipur Rajasthan India

This paper deals with the optimum two-plane balancing of the threshing drum using discrete parameters such as masses and corresponding angular locations on balance planes. To improve the dynamic performance characteristics of the thresher machine, the balancing of the threshing drum becomes an essential task for designers and engineers. Therefore, the optimization problem is formulated to minimize the reaction forces on bearings. The masses and corresponding angular locations on each balancing plane are treated as the design variables. Generally, continuous solutions of mass and corresponding angular position per plane are obtained by the continuous optimization algorithms for balancing. But these solutions are recalculated to the nearest discrete solutions due to a limited set of available standardized mass values and possible available locations. This process increases the balancing cost and time. Therefore, the modified jaya algorithm is developed to find the optimum discrete parameters for balancing. The proposed approach is investigated through a model of the threshing drum supported on bearings. The computation efficiency of the algorithm is compared with the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm with respect to the number of function evaluations. Adams software and experimental tests are used for validation of the proposed approach.

关键词： Balancing threshing drum jaya algorithm discrete parameters GA

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Stock market data modelling using fitness-oriented jaya algorithm-based deep belief network

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KYBERNETES 2020年第9期49卷 2309-2334页

作者： Swamy, A. Kullaya Sarojamma, B. SG Govt Degree & PG Coll Piler India Sri Venkateswara Univ Dept Stat Tirupati Andhra Pradesh India

Purpose - Data mining plays a major role in forecasting the open price details of the stock market. However, it fails to address the dimensionality and expectancy of a naive investor. Hence, this paper aims to study a future prediction model named time series model is implemented. Design/methodology/approach - In this model, the stock market data are fed to the proposed deep neural networks (DBN), and the number of hidden neurons is optimized by the modified jaya algorithm (JA), based on the fitness function. Hence, the algorithm is termed as fitness-oriented JA (FJA), and the proposed model is termed as FJA-DBN. The primary objective of this open price forecasting model is the minimization of the error function between the modeled and actual output. Findings - The performance analysis demonstrates that the deviation of FJA-DBN in predicting the open price details of the Tata Motors, Reliance Power and Infosys data shows better performance in terms of mean error percentage, symmetric mean absolute percentage error, mean absolute scaled error, mean absolute error, root mean square error, L1-norm, L2-Norm and Infinity-Norm (least infinity error). Research limitations/implications - The proposed model can be used to forecast the open price details. Practical implications - The investors are constantly reviewing past pricing history and using it to influence their future investment decisions. There are some basic assumptions used in this analysis, first being that everything significant about a company is already priced into the stock, other being that the price moves in trends Originality/value - This paper presents a technique for time series modeling using JA. This is the first work that uses FJA-based optimization for stock market open price prediction.

关键词： Stock market data Time series modelling Deep belief network jaya algorithm Error minimization

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Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and jaya algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第17期77卷 22629-22648页

作者： Zhang, Yu-Dong Zhao, Guihu Sun, Junding Wu, Xiaosheng Wang, Zhi-Heng Liu, Hong-Min Govindaraj, Vishnu Varthanan Zhan, Tianmin Li, Jianwu Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454000 Henan Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Kalasalingam Univ Dept Instrumentat & Control Engn Virudunagar Tamil Nadu India Nanjing Audit Univ Sch Technol Nanjing 211815 Jiangsu Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

Pathological brain detection is an automated computer-aided diagnosis for brain images. This study provides a novel method to achieve this *** first used synthetic minority oversampling to balance the dataset. Then, our system was based on three components: wavelet packet Tsallis entropy, extreme learning machine, and jaya algorithm. The 10 repetitions of K-fold cross validation showed our method achieved perfect classification on two small datasets, and achieved a sensitivity of 99.64 +/- 0.52%, a specificity of 99.14 +/- 1.93%, and an accuracy of 99.57 +/- 0.57% over a 255-image dataset. Our method performs better than six state-of-the-art approaches. Besides, jaya algorithm performs better than genetic algorithm, particle swarm optimization, and bat algorithm as ELM training method.

关键词： Pathological brain detection Synthetic minority oversampling Extreme learning machine jaya algorithm

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An enhanced multi-objective jaya algorithm for U-shaped assembly line balancing considering preventive maintenance scenarios

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第20期59卷 6146-6165页

作者： Zhang, Zikai Tang, Qiuhua Han, Dayong Qian, Xinbo Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Wuhan Peoples R China

Given that a U-shaped assembly line is usually regarded as a serial flow production system, preventive maintenance (PM) on machines in any station of the line may lead to stoppage of the whole line and sequentially cause great loss of production cost. To improve production continuity during PM, this paper proposes multiple alternative assignment plans with interchangeable abilities to ascertain the production recovery from changes of line structure. A mixed-integer mathematical model is further formulated to make decisions on alternative assignment plans. In this model, cycle time and total assignment plan alteration cost are minimised simultaneously. And an enhanced jaya algorithm is developed to obtain well-distributed Pareto frontier solutions effectively and efficiently. The proposed algorithm suggests a similarity-based selection and various crossover/neighbourhood search operators to enhance its search ability for global optima. Numerical experiment results prove the effectiveness of the proposed model and enhanced jaya. This novel methodology of integration optimisation bridges assembly line balancing and PM, and thus promotes production continuity and achieves economic benefits under maintenance within large-scaled assembly lines in real situations. Furthermore, a real-world case study is conducted to illustrate the significance of this novel methodology.

关键词： Assembly line balancing preventive maintenance jaya algorithm multi-objective optimisation U-shaped

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Parameter estimation of the soil water retention curve model with jaya algorithm

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2018年 151卷 349-353页

作者： Wang, Long Huang, Chao Huang, Lingmiao Univ Sci & Technol Sch Comp & Commun Engn Beijing 100083 Peoples R China Guangdong Prov Key Lab New & Renewable Energy Res Guangzhou 510640 Guangdong Peoples R China City Univ Hong Kong Dept Syst Engn & Engn Management Tat Chee Ave Kowloon Hong Kong Peoples R China China Agr Univ Coll Water Resources & Civil Engn 83 Qinghua East Rd Beijing 100083 Peoples R China

Parameter estimation of the soil water retention curve model using jaya algorithm is studied in this paper. Different from conventional heuristic search algorithms, jaya algorithm is a swarm intelligence algorithm without algorithm-specific parameters. The effectiveness of jaya algorithm is validated via estimating parameters of the Van Genuchten model based on collected experimental data of six soil samples. An optimization problem is formulated to minimize the difference between the measured and modeled water content while jaya algorithm is employed to solve this optimization problem. To assess the estimation performance, jaya algorithm is benchmarked with the Differential Evolution, RETC and Particle Swarm Optimization algorithms and computational results prove the superiority of the proposed method over state-of-the-art methods.

关键词： Parameter estimation Soil water retention curve Heuristic search jaya algorithm

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Modified jaya algorithm with depth search and perturbation

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2023年第3期17卷 276-283页

作者： Liu, Xiang Tang, Jun Hunan Urban Construction Coll Dept Architectural Engn Xiangtan 411101 Peoples R China Hunan Urban Construction Coll Dept Construction Equipment Engn Xiangtan 411101 Peoples R China

This paper presents a modified jaya algorithm with depth search and perturbation to solve numerical optimisation problems. The new jaya approach (called MDPjaya) consists of two improved strategies. First, the original search equation is modified by introducing a random solution. Second, a Cauchy perturbation based on the depth search is used to strengthen the global search ability. To validate the optimisation capability of MDPjaya, some famous benchmark optimisation problems are tested. Simulation results show MDPjaya can achieve better solutions than the standard jaya algorithm.

关键词： jaya algorithm modified search equation Cauchy perturbation depth search numerical optimisation

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