jaya algorithm
در نشریات گروه فنی و مهندسی-
در این مطالعه، طراحی مبدل حرارتی پوسته و لوله مبتنی بر نانوسیال برای اولین بار با استفاده از سه الگوریتم چند هدفه بهینه سازی شده است. دو شرایط عملیاتی مختلف برای مقایسه عملکرد الگوریتم ها بر اساس یک مدل اقتصادی (تابع هزینه) بررسی می شود. بر اساس نتایج به دست آمده، الگوریتم های بهینه سازی ژنتیک، ازدحام ذرات و جایا همگی می توانند طراحی را بهبود بخشند. میزان بهبود طراحی با روش های بهینه سازی ژنتیک، ازدحام ذرات و جایا به ترتیب 9.66%، 10.63% و 10.9% است. همچنین از نظر زمان بهینه سازی، الگوریتم بهینه سازی جایا نسبت به دو الگوریتم دیگر زمان پردازش نسبتا کمتری دارد که در واقع باعث کاهش هزینه های محاسباتی در محاسبات پیچیده می شود. در نهایت با توجه به عملکرد خوب الگوریتم بهینه سازی جایا در مقایسه با سایر الگوریتم های در نظر گرفته شده، عملکرد مبدل های حرارتی برای استفاده از نانوسیالات Ag، TiO2 و Al2O3 از 0.5% تا5%غلظت حجمی توسط این الگوریتم ارزیابی می شود. یک عامل ارزیابی عملکرد (PEC) به عنوان معیاری برای بررسی همزمان عملکرد حرارتی و هیدرولیکی نانوسیال ها معرفی شده است. نتایج نشان می دهد که نانوسیال نقره در میان سایر نانوسیال ها عملکرد بهتری دارد.In this study, the design of a nanofluid driven shell and tube heat exchanger is optimized, for the first time, by use of three multi objective algorithms. Two different operating conditions are investigated to compare the performance of the algorithms based on an economic model (cost function). Based on the obtained results, the Genetic, Particle Swarm and Jaya optimization algorithms can all improve the design. The amount of design improvement by each method is 9.66%, 10.63% and 10.9% respectively. Also from the view point of optimization time, Jaya optimization algorithm has relatively less CPU time than the other two algorithms, which in fact, reduces computational costs in complicated computations. Finally, due to the good performance of Jaya optimization algorithm in comparison with other considered algorithms, the performance of the heat exchangers is evaluated for using Ag, TiO2 and Al2O3 nanofluids of 0.5% to 5 vol.% by this algorithm. A performance evaluation factor (PE) is introduced as the criterion for simultaneous investigation of thermal and hydraulic performance of nanofluids. The results show that silver nanofluid, among other ones has better performance.Keywords: Heat exchanger, Genetic algorithm, Particle swarm, Jaya algorithm, Nanofluid, Multi Objective Optimization
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In order to minimise the difficulties associated in selecting conventional coolants in any machining, cutting fluids like vegetable based oils can serve as a viable alternative. Vegetable based oils when used in combination with eco-friendly techniques like MQL/NDM can have a major impact in any machining. In the present paper, performance characteristics of surface roughness and tool wear in machining of EN 36 steel alloy under Near Dry machining conditions/ Minimum quantity lubrication using vegetable based oil lubricant is studied. The input parameters like MQL flow rate, speed, feed and depth of cut for 5 levels are used in the CCD approach of Response surface methodology. For improving the machinability of alloy steel and to predit the values a regression equation is designed and developed between the input parameter and the output parameters. A multi-response optimum model for the output responses was also developed using RSM, GRA and JAYA algorithm, It was observed from the experiment results that JAYA algorithm has been proved the best multi-response optimization technique when compared to grey relational analysis and RSM.Keywords: Minimum Quantity lubrication, Vegetable based oil cutting fluids, Response Surface Methodology, Grey Relational Analysis, JAYA Algorithm
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در این تحقیق هدف کمینه کردن وزن سازه ی خرپا با به کارگیری فرمول بندی روش نیرو و الگوریتم بهینه سازی جایاست. قیود مسئله شامل قید تنش، جابه جایی و محدودیت سطح مقطع است و متغیرهای طراحی سطح مقطع اعضا در نظر گرفته شده اند. الگوریتم جایا یک الگوریتم جستجوی تصادفی است که برخلاف سایر الگوریتم های بهینه سازی برای تولید جمعیت به پارامترهای تنظیم کننده ی خاصی نیازی ندارد. بررسی مثال های موجود نشان از کارایی روش ارایه شده دارد. نتایج نشان می دهد که جواب نهایی به نقاط شروع حساس نیست و فقط باید نقاط شروع به حد کافی بزرگ باشند تا در داخل فضای قابل قبول طراحی قرار گیرند. برخلاف سایر روش های تصادفی که عموما نیاز به تعداد تکرار بسیار زیاد برای رسیدن به جواب دارند، در روش ارایه شده با تعداد چرخه ی به مراتب کمتر این امر حاصل شده است. همچنین نتایج نشان دهنده ی کاهش سریع وزن در چند چرخه ی اولیه است.
کلید واژگان: بهینه سازی، روش نیرو، سازه ی خرپا، الگوریتم جایاThis research aims to minimize the weight of truss structures using force method formulation as a structural analyzer and Jaya algorithm as an optimizer tool. Constraints considered herein include stress limitations, displacement limitations, and size limitations. Design variables include the cross-sectional area of each element. They may easily be related to each other which will lead to decrease of design variables.Jaya algorithm is a meta-heuristic random search method recently developed for constrained and unconstrained problems. The main superiority of Jaya algorithm to other random search methods is that it does not need any specific tuning parameter to generate next population. This algorithm consists of two steps in each cycle. First, a new population is generated using a simple random formula. Second, each new point is compared to its corresponding previous one while penalty function method is implemented. If the new point is in a better condition than the old one, the old one is replaced by the new one. All points are tested similarly till the population is updated. The procedure is repeated so as to achieve the desired convergence.Several landmark examples appearing in the literature have been solved by the proposed method, thus showing the efficacy of the developed procedure. A perusal of results shows that procedure is not sensitive to the starting points and it should just be selected large enough to lie in feasible-usable design space. Moreover, rapid reduction of weight is obtained in the first few steps and the tendency of decreasing of the weight appears to be monotonic and uniform in all examples.Unlike other metaheuristic methods that need a large number of optimization cycles to settle near the optimum point, combination of force method and Jaya algorithm provides higher computational efficiency and rapid convergence ability achieved by the above match. This is owing to the forced method formulation that makes the stress constraints to be linear, resulting in facilitating the procedure and enhancing its efficiency.
Keywords: Force Method, Jaya algorithm, Metaheuristic, truss structures, Random search -
Flexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having ‘g’ operations is performed on ‘g’ operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching–learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP.
Keywords: Flexible flow shop, JAYA algorithm, Makespan, Meta-heuristics, Teaching- learning-based optimization -
Material removal rate and surface roughness are the most important performance measures in nano-finishing processes and these are largely influenced by the process parameters. The optimum combination of process parameters for nano-finishing processes is determined in this paper using a recently proposed optimization algorithm, named as Jaya algorithm. The results show the better performance of the Jaya algorithm over the other approaches attempted by the previous researchers such as genetic algorithm and desirability function approach for the same nano-finishing processes. The results obtained by the Jaya algorithm are useful for the real production systems.Keywords: Nano, finishing processes, Parameters optimization, Jaya algorithm
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International Journal of Optimization in Civil Engineering, Volume:7 Issue: 2, Spring 2017, PP 269 -290The present study is concerned with the simultaneous optimization of the size of components and the arrangement of connections for performance-based seismic design of low-rise SPSWs. Design variables include the size of beams and columns, the thickness of the infill panels, the type of each beam-to-column connection and the type of each infill-to-boundary frame connection. The objective function is considered to be the sum of the material cost and rigid connection fabrication cost. For comparison purposes, the SPSW model is also optimized with regard to two fixed connection arrangements. To fulfill the optimization task a new hybrid optimization algorithm called CBO-Jaya is proposed. The performance of the proposed hybrid optimization algorithm is assessed by two benchmark optimization problems. The results of the application of the proposed algorithm to the benchmark problem indicate the efficiency, robustness, and the fast convergence of the proposed algorithm compared with other meta-heuristic algorithms. The achieved results for the SPSWs demonstrate that incorporating the optimal arrangement of beam-to-column and infill-to-boundary frame connections into the optimization procedure results in considerable reduction of the overall cost.Keywords: steel plate shear wall, a hybrid CBO, Jaya algorithm, optimal performance, based seismic design, connection arrangement
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