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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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Multi-skill resource constrained project scheduling using a multi-objective discrete jaya algorithm

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APPLIED INTELLIGENCE 2022年第5期52卷 5718-5738页

作者： Li, Yang-Yuan Lin, Jian Wang, Zhou-Jing Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China

Multi-skill resource constrained project scheduling problem (MS-RCPSP) is one of the widest investigated problems in manufacturing systems. This paper presents a multi-objective discrete jaya (MODjaya) algorithm to address the MS-RCPSP with the objectives of minimizing makespan and total cost simultaneously. In MODjaya, the best and worst solutions are used to generate offspring. Two simple heuristics are employed to enhance the exploration ability. An efficient encoding and decoding pair is designed to construct feasible schedules, and different assignment criteria are proposed to increase solution diversity. In addition, a left-shift scheme is embedded into the decoding process for improving resource utilization. The performance of the proposed algorithm is evaluated on an intelligent multi-objective project scheduling environment benchmark dataset. Computational results and comparisons with the state-of-the-art algorithms indicate the superiority of the MODjaya in solving the multi-objective MS-RCPSP.

关键词： Multi-objective jaya algorithm Multi-skill Project scheduling

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Modular energy management system with jaya algorithm for hybrid energy storage in electric vehicles

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第15期46卷 21497-21510页

作者： Demircali, Akif Koroglu, Selim Pamukkale Univ Dept Elect & Elect Engn TR-20160 Denizli Turkey

Increasing the energy efficiency in hybrid energy storage-based electric vehicles is among the important research areas. Energy sharing in such vehicles is possible with an efficient and optimum energy management system. In this study, a jaya-based modular energy management system is proposed to optimize power sharing between battery and ultracapacitor for an electric vehicle. In this system, battery is connected to DC bus directly, and ultracapacitor is connected via a bidirectional DC-DC converter. This converter provides the power flow between the DC bus and the ultracapacitor as well as perform the proposed energy management system algorithm. The designed modular energy management system has the feature of being easily integrated into a battery-only electric vehicle, and two-way power transfer between the battery and the ultracapacitor has been successfully achieved. At the same time, real-time implementation of jaya-based energy management system is experimentally tested for the first time and compared to rule-based energy management system. The proposed jaya-based energy management system reduces the total loss amount by 24.5% and the battery current root mean square value by 28.8% compared to the rule-based method. The results show that the proposed method can be successfully applied for real-time energy management system applications.

关键词： battery electric vehicle jaya algorithm modular energy management system ultracapacitor

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Parameter identification of airfoil systems using an elite-based clustering jaya algorithm and incremental vibration responses

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2022年第7期65卷 209-209页

作者： Ding, Zhenghao Zhang, Yuxuan Lu, Zhongrong Xia, Yong Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Sun Yat Sen Univ Dept Mech Guangzhou 510005 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Hubei Peoples R China

Airfoil systems generally exhibit a variety of nonlinearity under different wind speeds. To effectively draft strategies to restrain undesirable nonlinearity, airfoil systems' parameters must be figured out beforehand. In this article, a novel cluster-based jaya algorithm is proposed to identify airfoil systems' dimension parameters, nonlinear parameters, and vibration frequencies. In the proposed algorithm, the improvement is focused on introducing the elite-based clustering framework to balance the algorithm's exploration and exploitation to enhance the convergence rate. To improve the effectiveness and efficiency of the proposed algorithm, eight benchmark functions are introduced to test and compared with other latest optimizations. The comparison results show that the proposed algorithm has better performance in convergence rate and accuracy. Afterward, the proposed algorithm is applied to identify airfoil systems by minimizing the incremental acceleration response-based objective function. Different wind speeds are considered in the numerical simulation of the airfoil system, which reveals bifurcation, quasi-periodic oscillation, and chaos. In all cases, the proposed method yields accurate and robust results even when noisy data are used.

关键词： Airfoil system Parameter identification Nonlinear vibration jaya algorithm

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An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and jaya algorithm for parameter extraction of photovoltaic models

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第6期19卷 5610-5637页

作者： Zhang, Yujun Wang, Yufei Li, Shuijia Yao, Fengjuan Tao, Liuwei Yan, Yuxin Zhao, Juan Gao, Zhengming Jingchu Univ Technol Sch Elect & Informat Engn Jingmen 448000 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Foreign Languages Jingmen 448000 Peoples R China Jingchu Univ Technol Acad Arts Jingmen 448000 Peoples R China Hubei Jingmen Ind Technol Res Inst Inst Intelligent Informat Technol Jingmen 448000 Peoples R China

In order to maximize the acquisition of photovoltaic energy when applying photovoltaic systems, the efficiency of photovoltaic system depends on the accuracy of unknown parameters in photovoltaic models. Therefore, it becomes a challenge to extract the unknown parameters in the photovoltaic model. It is well known that the equations of photovoltaic models are nonlinear, and it is very difficult for traditional methods to accurately extract its unknown parameters such as analytical extraction method and key points method. Therefore, with the aim of extracting the parameters of the photovoltaic model more efficiently and accurately, an enhanced hybrid jaya and Rao-1 algorithm, called EHRjaya, is proposed in this paper. The evolution strategies of the two algorithms are initially mixed to improve the population diversity and an improved comprehensive learning strategy is proposed. Individuals with different fitness are given different selection probabilities, which are used to select different update formulas to avoid insufficient using of information from the best individual and overusing of information from the worst individual. Therefore, the information of different types of individuals is utilized to the greatest extent. In the improved update strategy, there are two different adaptive coefficient strategies to change the priority of information. Finally, the combination of the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed of the algorithm and ability to escape from local optimum can be improved. The results of various experiments prove that the proposed EHRjaya has superior performance and rank in the leading position among the famous algorithms.

关键词： parameter extraction photovoltaic models jaya algorithm Rao-1 algorithm EHRjaya algorithm

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Multi-strategy jaya algorithm for industrial optimization tasks

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 4379-4393页

作者： Yu, Xiaobing Luo, Wenguan Rao, R. Venkata Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing Peoples R China SV Natl Inst Technol Dept Mech Engn Surat Gujarat India

jaya, a simple heuristic algorithm, has shown attractive features, especially parameter-free. However, the simple structure of jaya algorithm may result in poor performances, to boost the performance, a multi-strategy jaya (Mjaya) algorithm based on multi-population has been proposed in this paper. Three strategies correspond to three groups of solutions. The first strategy based on the first population is to introduce an adaptive weight parameter to the position-updating equation to improve the local search. The second strategy is based on rank-based mutation to enhance the global search. The third strategy is to exploit around the best solution to reinforce the local search. Three strategies cooperate well during the evolution process. The experimental results based on CEC 2014 have proven that the proposed Mjaya is superior compared with jaya and its latest variants. Then, the proposed Mjaya algorithm is used to solve three industrial problems and the results have shown that the proposed Mjaya algorithm can also solve complex industrial applications effectively.

关键词： jaya algorithm multi-strategy optimization adaptive

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Powerful enhanced jaya algorithm for efficiently optimizing numerical and engineering problems

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SOFT COMPUTING 2022年第11期26卷 5315-5333页

作者： Gholami, Jafar Kamankesh, Mohamad Reza Mohammadi, Somayeh Hosseinkhani, Elahe Abdi, Somayeh Islamic Azad Univ Dept Comp Engn Kermanshah Sci & Res Branch Kermanshah Iran Islamic Azad Univ Dept Comp Engn Borujerd Branch Lorestan Iran Islamic Azad Univ Dept Comp Engn Kermanshah Branch Kermanshah Iran Islamic Azad Univ Dept Comp Engn Eslam Abade Gharb Branch Eslamabad E Gharb Iran FreieUniv Berlin Dept Math & Comp Sci Berlin Germany

Over the last decade, the size and complexity of real-world problems have grown dramatically, necessitating more effective tools. Nature-inspired metaheuristic algorithms have proven to be a promising tool for solving such problems due to their performance in a variety of fields. jaya algorithm is a novel population-based algorithm which could have been able to present reliable results. This is because it does not need any parameters to be set other than the population size and the maximum number of iteration. Despite its positive feedbacks, this algorithm should be modified to witness more efficiency. This paper aims to amend the original version of jaya to present a high-efficiency version named Powerful Enhanced jaya (PEjaya). In other words, the methodology of updating position in jaya is modified to enhance the convergence and search capabilities. This approach is assessed according to solve 20 well-known benchmark functions, feature selection, and statistical tests. The output results of proposed optimization algorithm are then evaluated by comparing it with other recent algorithms including crow search algorithm (CSA), standard version of jaya, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO) and sine-cosine algorithm (SCA). Solving a real-world problem is another way of checking the efficiency of this approach with other published works. Prompt escape from local minima, superior convergence, and stability demonstrate that the suggested approach is a very powerful instrument that may be employed in a variety of optimization situations.

关键词： jaya algorithm Improvement Real-world evaluation Benchmark functions

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A dynamic load scheduling in IaaS cloud using binary jaya algorithm

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第8期34卷 4914-4930页

作者： Mishra, Kaushik Pati, Jharashree Majhi, Santosh Kumar Veer Surendra Sai Univ Technol Dept Comp Sci & Engn Burla India

Datacenters receive dynamic workloads with disparate specifications to be scheduled on virtual machi-nes (VMs). These unpredictable, alarmingly growing workloads with varying resource specifications may bring down the servers of datacenters into an imbalanced state. Thus, resulting in low resource utilization and high energy consumption among the servers. To cater to the need of fluctuating on-demand resource provisioning, it is essential to scale up the ability and capacityof existing infrastructure through virtual-ization. Moreover, due to the involvement of conflicting scheduling constraints, load scheduling in cloud computing fall under NP-hard problem. An effective scheduling mechanism in amalgamation with a load balancing strategy based on binary jaya is implemented to alleviate the challenges as mentioned above. This technique not only improves resource utilization but also brings down the degree of energy con-sumption and makespan while keeping the whole system balanced. At first, it focuses on evoking a load balancing procedure to uniformly disperse the loads among VMs based on the compatibility between the tasks and VMs and secondly, jaya algorithm is executed to find the best possible mapping of tasks onto VMs. In order to appraise the efficacy of the proposed algorithm, the scheduling of independent and non -preemptive tasks is simulated in CloudSim using a benchmark parallel workload by NASA-iPSC. Experiments are conducted in both homogenous and heterogeneous environments. The proposed algo-rithm is statistically validated using the Friedman test, followed by a Holm's test. The proposed approach is compared over other algorithms such as Round Robin (RR), binary Particle Swarm Optimization (PSO) and Genetic algorithm (GA). The simulation results show a notable amelioration over other mentioned algorithms by an increase of resource utilization with 18.47% (GA), 12.65% (BPSO) and 4.18% (GA), 2.51% (BPSO) and a reduction of makespan by 6.47% (GA), 4.35% (BP

关键词： Cloud computing Task scheduling Load balancing Makespan jaya algorithm Resource utilization

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An improved quadratic approximation-based jaya algorithm for two-echelon fixed-cost transportation problem under uncertain environment

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SOFT COMPUTING 2022年第19期26卷 10301-10320页

作者： Das, Rajeev Das, Kedar Nath Mallik, Saurabh Natl Inst Technol Silchar Dept Math Silchar 788010 Assam India Natl Inst Technol Silchar Dept Comp Sci & Engn Silchar 788010 Assam India

The objective of a fixed-cost transportation problem (FCTP) is to minimize the total transportation cost (or to maximize the total profit). FCTPs become more complex due to the presence of uncertain factors like suppliers' capacity, customers' demand, fixed cost and amount of goods transported. In order to deal with its complexity, three mathematical models, namely the expected value, chance-constrained and measure chance models, are basically available in the literature. This paper aims to solve the crisp equivalent models of an uncertain two-echelon FCTP by employing a robust hybrid evolutionary algorithm. The framework of the designed algorithm comprises of parameter-free jaya algorithm and the efficient quadratic approximation operator. Initially, its efficiency is validated on the 24 constrained benchmark functions from CEC 2006 (Liang et al. in Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization, 2006) and 30 unconstrained benchmark functions (Rao in Int J Ind Eng Comput 7:19-34, 2016). Friedman rank test and Iman-Davenport test are conducted for both constrained and unconstrained test instances. Additionally, five real-life engineering design problems are solved to further analyze the flexibility of the proposed algorithm. The computational results and convergence graphs justify the robustness of the designed algorithm over state-of-the-art algorithms. Finally, its application is demonstrated on chance-constrained and measure chance two-echelon FCTP models. Near-optimal results are contradicted by genetic algorithm and particle swarm optimization in solving both these models. Further, a few large-scale instances of FCTP are solved by the proposed algorithm.

关键词： Normal uncertain variable Fixed-cost transportation problem Benchmark functions jaya algorithm Quadratic approximation

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Parameter identification for a three-dimensional aerofoil system considering uncertainty by an enhanced jaya algorithm

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ENGINEERING OPTIMIZATION 2022年第3期54卷 450-470页

作者： Ding, Z. H. Lu, Z. R. Chen, F. X. Hong Kong Polytech Univ Dept Civil & Environm Engn Kowloon Hong Kong Peoples R China Sun Yat Sen Univ Dept Mech Guangzhou Peoples R China Guangdong Polytech Normal Univ Guangzhou Peoples R China Guangxi Univ Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning Peoples R China

Parameter identification for an aerofoil with an external store system based on an enhanced jaya algorithm is proposed. The discrepancy between the measured and calculated displacement responses is used to formulate the objective function, which will be optimized by the enhanced jaya algorithm. In the enhanced jaya algorithm, two variants are introduced: the k-means clustering is employed to utilize the colony information fully;and a new updating equation that focuses on the exploration search is designed for the best-so-far solution in each cycle. During the identification process, both the inherent and extrinsic uncertainties existing in the aerofoil system are considered. The final identification results show that owing to its strong global search ability and robustness, the proposed enhanced jaya can clearly identify the aerofoil system, even when the system is under quasi-periodic oscillations. The identification accuracy is better than that acquired by other approaches.

关键词： Aerofoil quasi-periodic oscillations jaya algorithm uncertainties displacement responses

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