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A Prediction Model-Guided jaya algorithm for the PV System Maximum Power Point Tracking

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2018年第1期9卷 45-55页

作者： Huang, Chao Wang, Long Yeung, Ryan Shun-Cheung Zhang, Zijun Chung, Henry Shu-Hung Bensoussan, Alain City Univ Hong Kong Dept Syst Engn & Engn Management Kowloon Hong Kong Peoples R China City Univ Hong Kong Ctr Smart Energy Convers & Utilizat Res Kowloon Hong Kong Peoples R China Univ Texas Dallas Jindal Sch Management Richardson TX 75080 USA

This paper proposes a novel model-free solution algorithm, the natural cubic-spline-guided jaya algorithm (S-jaya), for efficiently solving the maximum power point tracking (MPPT) problem of PV systems under partial shading conditions. A photovoltaic (PV) system which controls the power generation with its operating voltage is considered. As the same as the generic jaya algorithm, the S-jaya is free of algorithm-specific parameters. A natural cubic-spline-based prediction model is incorporated into the iterative search process to guide the update of candidate solutions (operating voltage settings) in the S-jaya and such extension is capable of improving the tracking performance. Simulation studies and experiments are conducted to validate the effectiveness of the proposed S-jaya algorithm for better addressing PV MPPT problems considering a variety of partial-shading conditions. The performance of the proposed algorithm is benchmarked against the generic jaya and the particle swarm optimization, which has been widely considered in themodel-free MPPT, to demonstrate its advantages. Results of simulation studies and experiments demonstrate that the S-jaya algorithm converges faster and provides a higher overall tracking efficiency.

关键词： Heuristic search jaya algorithm maximum power point tracking partial shading conditions photovoltaic system

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Optimization of Reservoir Operation with a New Approach in Evolutionary Computation Using TLBO algorithm and jaya algorithm

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WATER RESOURCES MANAGEMENT 2018年第13期32卷 4375-4391页

作者： Kumar, Vijendra Yadav, S. M. Sardar Vallabhbhai Natl Inst Technol Civil Engn Dept Surat Gujarat India

Reservoir operation and management are complex engineering problems, due to the stochastic nature of inflow, various demands and as well as tailwater in the downstream. The complexity increases when the number of reservoirs gets increased such as multi-reservoir system or chain system. To obtain optimal operation in such condition become more difficult. It requires powerful optimization algorithm to solve aforesaid problems. Teaching Learning Based Optimization (TLBO) algorithm and jaya algorithm (JA) are recently developed advanced optimization techniques a novel approach comparatively simple, easy, and robust. The main advantages of these algorithms are it only requires the common control parameters such as number of iterations and population size. In the present study, three different benchmark problems were evaluated to check the applicability and performance of TLBO and JA in multireservoir operation problems. The benchmark problems are the discrete time four-reservoir operation (DFRO), the continuous time four-reservoir operation (CFRO), and the ten-reservoir operation (TRO). The results from the TLBO and JA are compared with different approaches from the literature. The optimal net benefits obtained from JA for DFRO, CFRO and TRO problems are 401.44, 308.40 and 1194.59, respectively, and that of TLBO algorithm are 401.33, 308.30 and 1194.44, respectively. It is found that both JA and TLBO algorithms provided a satisfactory solution as other optimization techniques, from literature. In conclusion, JA outperformed over TLBO.

关键词： TLBO jaya algorithm LINGO software Four-reservoir operation Linear programming Ten-reservoir operation

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Sensing period adaptation for multiobjective optimisation in cognitive radio using jaya algorithm

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ELECTRONICS LETTERS 2017年第19期53卷 1335-1336页

作者： Kaur, A. Sharma, S. Mishra, A. Thapar Univ Dept Elect & Commun Engn Patiala 147004 Punjab India

Adaptation of sensing period for licensed channel is one of the key requirements to maximise the utilisation of transmission opportunities by a secondary user. A novel objective function evaluation method for sensing period optimisation in cognitive radio network is formulated. A recently introduced optimisation technique, namely jaya algorithm has been used for achieving the optimum value of sensing period in order to maximise the discovery of spectrum opportunities while maintaining the sensing overhead and interference time within user defined value. This multiobjective optimisation problem involving different constraints is solved using an objective function that introduces penalty for violation of constraints. Results show that the proposed scheme leads to quick adaptation and efficient discovery of spectrum opportunities while satisfying the different QoS constraints, thus making it a better choice for real-time applications.

关键词： cognitive radio wireless channels optimisation radio spectrum management sensing period adaptation multiobjective optimisation problem cognitive radio network jaya algorithm licensed channel secondary user objective function evaluation method sensing period optimisation spectrum opportunities discovery maximization sensing overhead interference time user defined value QoS constraints

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Structural damage identification considering uncertainties based on a jaya algorithm with a local pattern search strategy and L0.5 sparse regularization

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ENGINEERING STRUCTURES 2022年 261卷 1页

作者： Ding, Zhenghao Hou, Rongrong Xia, Yong Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Harbin Inst Technol Sch Civil Engn Harbin Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Hubei Peoples R China

This study develops an improved jaya algorithm for structural damage identification considering various uncertainties using vibration data. Most studies consider uncertainties, such as measurement noise, modeling uncertainties, and temperature variations separately but few on their coupling effects. On the other hand, the jaya algorithm may trap local minimums in optimizing complex objective functions. Therefore, a novel modified jaya algorithm is developed by integrating the one-step K-means clustering, Hooke-Jeeves pattern search, and linear population reduction strategies. The three modification strategies greatly improve the global optimization performance of the standard jaya algorithm, which is verified by a series of high-dimension test functions. An L-0.5 regularization is applied to improve the ill-posedness of the damage identification problem and ensure the sparsity of the solution. Numerical and experimental studies on two structures show that the proposed algorithm can identify the structural damage accurately, even considering the coupling effects of various types of uncertainties.

关键词： Structural damage identification jaya algorithm Temperature variations L-0.5 regularization item Coupling effects

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An Adaptive Opposition-Based Learning Selection: The Case for jaya algorithm

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IEEE ACCESS 2021年 9卷 55581-55594页

作者： Nasser, Abdullah B. Zamli, Kamal Z. Hujainah, Fadhl Ghanem, Waheed Ali H. M. Saad, Abdul-Malik H. Y. Alduais, Nayef Abdulwahab Mohammed Univ Malaysia Pahang Coll Comp & Appl Sci Fac Comp Pekan 26600 Malaysia Chalmers & Univ Technol Comp Sci & Engn Dept S-41296 Gothenburg Sweden Univ Malaysia Terengganu Fac Ocean Engn Technol & Informat Kuala Terengganu 21030 Malaysia Univ Teknol Malaysia Fac Engn Sch Elect Engn Div Elect & Comp Engn Johor Baharu 81310 Malaysia Univ Tun Hussein Onn Malaysia UTHM Fac Comp Sci & Informat Technol FSKTM Parit Raja 86400 Malaysia

Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search space. In the last decade, many OBL techniques have been established in the literature including the Standard-OBL, General-OBL, Quasi Reflection-OBL, Centre-OBL and Optimal-OBL. Although proven useful, much existing adoption of OBL into meta-heuristic algorithms has been based on a single technique. If the search space contains many peaks with potentially many local optima, relying on a single OBL technique may not be sufficiently effective. In fact, if the peaks are close together, relying on a single OBL technique may not be able to prevent entrapment in local optima. Addressing this issue, assembling a sequence of OBL techniques into meta-heuristic algorithm can be useful to enhance the overall search performance. Based on a simple penalized and reward mechanism, the best performing OBL is rewarded to continue its execution in the next cycle, whilst poor performing one will miss cease its current turn. This paper presents a new adaptive approach of integrating more than one OBL techniques into jaya algorithm, termed OBL-JA. Unlike other adoptions of OBL which use one type of OBL, OBL-JA uses several OBLs and their selections will be based on each individual performance. Experimental results using the combinatorial testing problems as case study demonstrate that OBL-JA shows very competitive results against the existing works in term of the test suite size. The results also show that OBL-JA performs better than standard jaya algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process.

关键词： Software algorithms Optimization Genetic algorithms Explosions Artificial neural networks Search problems Reinforcement learning Opposition based learning adaptive selection jaya algorithm

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Modified jaya algorithm for Machinability Assessment and Conflicting Response Optimization During Milling of Nanostructured Carbon Onions Reinforced Epoxy Composites

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EXPERIMENTAL TECHNIQUES 2022年第6期46卷 1007-1025页

作者： Kesarwani, S. Verma, R. K. Debnath, K. Madan Mohan Malaviya Univ Technol Dept Mech Engn Mat & Morphol Lab Gorakhpur 273010 Uttar Pradesh India Natl Inst Technol Dept Mech Engn Shillong 793003 Meghalaya India

Polymer composites are extensively accepted in the material fraternity due to their lightweight, high-strength quality. The carbon nanomaterials (CMNs) significantly enhance the thermal resistivity, anti-corrosion, mechanical, and physical features of polymer (epoxy) composites. This paper emphasizes the machining aspects of Zero-dimensional Carbon nano onion (0D-CNO) reinforced polymer composites. The influence of four varying constraints, namely, weight% of CNO, Spindle speed, feed rate, and depth of cut, was examined through the Grey integrated jaya algorithm (GRA- jaya). The desired value of Surface roughness (Ra) and Material Removal Rate (MRR) was controlled through process constraints. The Milling experimentation was executed through the Taguchi L-27 orthogonal array (OA). The conflicting machining performances were aggregated by Grey theory to develop the algorithm fitness function. To obtain a better work efficiency, higher spindle speed, feed rate, depth of cut, and lower weight% of nanomaterial addition are proposed in this study. Hence, the result demonstrated that the weight% of CNO reinforcement and feed rate are the most influential factors for optimal machining performance results. The finding of the proposed module has compared with other metaheuristics algorithms, which demonstrate the higher application potential to acquire the desired machining characteristics. It can be suggested for the enhancement of quality and productivity indices of the polymer manufacturing sector.

关键词： jaya algorithm Grey CNO Polymer Milling

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Design optimization and analysis of selected thermal devices using self-adaptive jaya algorithm

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ENERGY CONVERSION AND MANAGEMENT 2017年第May期140卷 24-35页

作者： Rao, R. V. More, K. C. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

The present study explores the use of an improved jaya algorithm called self-adaptive jaya algorithm for optimal design of selected thermal devices viz;heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self adaptive jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Thermal devices Optimization jaya algorithm Self-adaptive jaya algorithm

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Coordinated control of conventional power sources and PHEVs using jaya algorithm optimized PID controller for frequency control of a renewable penetrated power system

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Protection and Control of Modern Power Systems 2019年第1期4卷 355-367页

作者： Anil Annamraju Srikanth Nandiraju Research Scholar Department of Electrical EngineeringNational Institute of Technology WarangalWarangalTelangana506004India

In renewable penetrated power systems, frequency instability arises due to the volatile nature of renewable energy sources (RES) and load disturbances. The traditional load frequency control (LFC) strategy from conventional power sources (CPS) alone unable to control the frequency deviations caused by the aforementioned disturbances. Therefore, it is essential to modify the structure of LFC, to handle the disturbances caused by the RES and load. With regards to the above problem, this work proposes a novel coordinated LFC strategy with modified control signal to have Plug-in Hybrid Electric Vehicles (PHEVs) for frequency stability enhancement of the Japanese power system. Where, the coordinated control strategy is based on the PID controller, which is optimally tuned by the recently developed jaya algorithm (JA). Numerous simulations are performed with the proposed methodology and, the results have confirmed the effectiveness of a proposed approach over some recent and well-known techniques in literature. Furthermore, simulation results reveal that the proposed coordinated approach significantly minimizing the frequency deviations compared to the jaya optimized LFC without PHEVs & with PHEVs but no coordination.

关键词： PHEVs Optimized PID controller Coordinated control Load frequency control jaya algorithm

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Parameters extraction of solar cells using modified jaya algorithm

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OPTIK 2020年 203卷 164034-164034页

作者： Luu, The Vinh Nguyen, Ngoc Son Ind Univ Ho Chi Minh City Fac Elect Technol Ho Chi Minh City Vietnam

Solar energy has been receiving tremendous interest in this decade, due to their broad applications in areas of renewable energy sources. To identify the accurate modeling of current and voltage characteristics of solar cells will help to improve the performance of solar energy systems. However, the I-V characteristics are multimodal and complex non-linear equations making these optimization techniques to get trapped into local optima, low efficiency due to an initial solution far from the global optimal solution. Therefore, this paper proposes a modified jaya (MJA) algorithm for estimating the solar cells parameters. In MJA, the modified solution updating phase by hybrid classical jaya operator with differential evolution (DE/rand/1) operator, and self-adaptive population size are employed to improve global searching ability. In order to validate the feasibility and performance of the proposed MJA algorithm, experimental studies are carried out in identifying the parameters of a single diode, and double diode models and full comparison with different variants of jaya, DE and other well-established algorithms in the last two years. Experimental results demonstrate that MJA can obtain better performance in terms of robustness and accuracy than all compared algorithms.

关键词： jaya algorithm Differential evolution algorithm Parameters identification Solar cell modeling

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An efficient jaya algorithm with Powell's Pattern Search for optimal power flow incorporating distributed generation

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ENERGY SOURCES PART B-ECONOMICS PLANNING AND POLICY 2021年第8期16卷 759-786页

作者： Gupta, Saket Kumar, Narendra Srivastava, Laxmi Delhi Technol Univ Elect Engn Dept Delhi 110042 India Madhav Inst Sci & Technol Dept Elect Engn Gwalior India

Optimal power flow solutions are required for the cost-effective operation and control of an existing power system as well as for its future expansion planning. In this paper, a hybrid meta-heuristic jaya-Powell's Pattern Search method is proposed to solve the optimal power flow problem integrated with distributed generating units. Powell's Pattern Search method has been incorporated into the jaya algorithm in three different ways, resulting in three variants namely;jaya-PPS1, jaya-PPS2, and jaya-PPS3. Performance of these variants are evaluated by solving optimal power flow in IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. In all the cases, jaya-PPS1 is found to provide a lower objective function value with a smaller deviation over multiple runs than other algorithms. For example, the combined objective function found by jaya-PPS1 for IEEE 30-bus system is 937.340, with a reduction of 2.86% of the original system. This benefit increases further with system size.

关键词： Distributed generation hybrid jaya-PPS algorithm jaya algorithm meta-heuristic optimal power flow PPS

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