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A comparative study on using metaheuristic algorithms for simultaneously estimating parameters of space fractional advection-dispersion equation

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JOURNAL OF HYDROLOGY 2021年 602卷 126757-126757页

作者： Maroufi, Halimeh Mehdinejadiani, Behrouz Univ Kurdistan Fac Agr Dept Water Sci & Engn Sanandaj Iran

This work developed four inverse models based on Particle Swarm optimization (PSO), Chicken Swarm optimization (CSO), Bees Algorithm (BA), and teaching learning based optimization (TLBO), to identify parameters of space fractional advection-dispersion equation (s-FADE). The s-FADE has four parameters, including average pore-water velocity (nu), fractional dispersion coefficient (D-f), fractional derivative order (alpha), and skewness (beta). A sensitivity analysis indicated that the v is the most effective parameter on the s-FADE results, followed by alpha, D-f, and beta, respectively. The experimental data required were measured at different transport distances of homogeneous and heterogeneous soil columns. Five criteria, namely, convergence trend, objective function value, runtime, repeatability of results, and modeling complexity were used to evaluate algorithm performances and to rank them using a technique for order of preference by similarity to ideal solution (TOPSIS). based on the obtained results, all four algorithms acquired the global optimal values for the nu and alpha parameters using a maximum iteration of 1000 as a stopping criterion and an initial population of 10, while they obtained the relatively different values for the D-f, and beta parameters. The PSO and TLBO algorithms successfully found the global minimum values of the objective functions for both the homogeneous and heterogeneous soils. Among the four algorithms, the TLBO algorithm was the best one in terms of convergence trend, repeatability of results, and modeling complexity, and it was the worst algorithm in term of runtime. Among the PSO, CSO, and BA algorithms, the BA algorithm was superior over the PSO and CSO algorithms in terms of runtime and repeatability of results, while the PSO algorithm was superior over the BA and CSO algorithms in term of converge speed. Overall, according to the results of the TOPSIS method, the TLBO algorithm was the best alternative to estimate the s-FADE

关键词： FracFit toolbox Non-fickian transport Gradient-based optimization algorithms Inverse problem method teaching learning based optimization TOPSIS method

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Estimating the non-uniform air velocity distribution for the optimal design of a heat exchanger

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APPLIED THERMAL ENGINEERING 2019年 153卷 704-714页

作者： Hajabdollahi, Zahra Hajabdollahi, Hassan Kim, Kyung Chun Pusan Natl Univ Sch Mech Engn Busan 609735 South Korea Vali E Asr Univ Rafsanjan Dept Mech Engn Rafsanjan *** Iran

The maldistribution of flow in a heat exchanger usually has negative effects on the heat exchanger specifications such as the effectiveness, annual cost, and pressure drop. This paper examines the issue to improve the thermoeconomic performance. For this purpose, a plate-fin heat exchanger was selected and optimized by considering both the effectiveness and total annual cost as objective functions. The design parameters were the parameters of the heat exchanger and fin, as well as the non-uniform inlet flow profiles for both sides of the hot and cold streams. To generalize the results, the optimization was applied at different mass flow rates. The optimum results of the non-uniform profile were compared with those obtained with a uniform profile of the inlet flow. The optimum results reveal that thermoeconomic improvement occurred in the case of non-uniform flow compared with the case of uniform flow. For example, a 12.7% reduction for the total annual cost was found in the case of non-uniform flow in comparison to the uniform flow for a fixed effectiveness of 0.92. At low effectiveness, no significant improvement was found in the case of non-uniform flow. Nevertheless, the suggested profiles could be considered as an alternative to uniform inlet profiles.

关键词： teaching learning based optimization Plate-fin heat exchanger Flow maldistribution Uniform flow Optimum inlet flow profile

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Thermo-hydraulic performance of roughened solar air heater by design of experiment and meta-heuristic approach

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2019年 10卷 92-102页

作者： Kumar, Anup Layek, Apurba NIT Durgapur Mech Engn Dept Durgapur 713209 India

The present investigation is undertaken with an objective to maximize thermo-hydraulic performance of solar air heater providing with twisted rib roughness. The optimization of various roughness parameters is done by implementing design of experiment (response surface methodology) and meta-heuristic (particle swarm optimization, teaching learning based optimization) approach for determining optimal thermo-hydraulic performance. The experimental work is conducted using Box Behnken design for 2nd order quadratic model. A mathematical model is developed for thermo-hydraulic performance in terms of roughness parameters e.g. pitch-height ratio (P/e), rib orientation (a), twist ratio (y/e) and Reynolds number (Re). The exorbitantly responsibility of rib roughness parameters on thermo-hydraulic performance is also discussed. The final optimum set of result for roughness parameters obtained by response surface methodology, particle swarm optimization, teaching learning based optimization technique are tested with experimental result to validate its robustness, flexibility and authenticity. The simulated optimized results obtained from teaching learning based optimization approaches exhibits quite satisfying with experimental data as far as competence, execution, reliability and global convergence.

关键词： Solar air heater Twisted rib roughness Response surface methodology Particle swarm optimization teaching learning based optimization

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Multiobjective optimization for improving machinability of Ti-6Al-4V using RSM and advanced algorithms

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2019年第1期6卷 1-12页

作者： Sahu, Neelesh Kumar Andhare, Atul B. Shri Ramdeobaba Coll Engn & Management Dept Ind Engn Nagpur 440013 Maharashtra India Visvesvaraya Natl Inst Technol Dept Mech Engn Nagpur 440010 Maharashtra India

This paper explores use of teaching learning based optimization (TLBO), 'JAYA' (Sanskrit word means Victory) and Genetic Algorithm (GA) for the combined minimization of roughness of machined surface and forces generated in cutting in turning of Ti-6Al-4V. Experimentation was carried out with Response Surface Methodology (RSM) and the Central Composite Design (CCD). Speed of cutting (m/min), feed rate (mm/min) and depth of cut (mm) were the design variables for optimization. Two responses (roughness of machined surface and force of cutting) were independently minimized. RSM was useful in finding empirical relations and the effect of each parameter and their interactions on the responses considered. Analysis of variance (ANOVA) was used to find out the effective and non-effective factors and correctness of the models. Later on, a multi-objective optimization function was developed for minimizing both -roughness in machined surface and force generated in cutting using weights method and the correctness of weights were confirmed by Analytical Hierarchy Process (AHP). After formulating the combined objective function, TLBO, 'JAYA' and GA methods were used for further parameter optimization of the turning process. Performance of TLBO and 'JAYA' algorithm was compared with that of Genetic Algorithm (GA). It is found that TLBO and 'JAYA' performed better than GA in the combined minimization of roughness and forces in while turning Ti-6Al-4V. It is also found from the results that higher cutting speed (171.4 m/min) and lower feed rate (55.6 mm/min) can produce better surface roughness and minimum cutting forces in machining of Ti-6Al-4V. (C) 2018 Society for Computational Design and Engineering. Publishing Services by Elsevier.

关键词： Titanium alloys Response surface methodology teaching learning based optimization 'JAYA' Cutting force Surface roughness

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Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

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WATER 2020年第4期12卷 1041页

作者： Nacar, Sinan Bayram, Adem Baki, Osman Tugrul Kankal, Murat Aras, Egemen Karadeniz Tech Univ Dept Civil Engn Fac Engn TR-61080 Trabzon Turkey Karadeniz Tech Univ Dept Civil Engn Fac Technol TR-61830 Trabzon Turkey Bursa Uludag Univ Fac Engn Dept Civil Engn TR-16059 Bursa Turkey Bursa Tech Univ Dept Civil Engn Engn & Nat Sci Fac TR-16310 Bursa Turkey

The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The

关键词： dissolved oxygen Eastern Black Sea Basin multivariate adaptive regression splines stream water quality teaching learning based optimization

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Coordinated Tuning of SVC and PSSs in Power System using teaching learning based Algorithm 10

Coordinated Tuning of SVC and PSSs in Power System using Tea...

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10th International Renewable Energy Congress (IREC)

作者： Welhazi, Yosra Guesmi, Tawfik Hadj Abdallah, Hsan ENIS Natl Engn Sch Sfax Control & Energies Management CEM Lab Sfax Tunisia

ISBN: (纸本)9781728101392

This paper presents the application of the teaching learning based optimization (TLO) for coordinated tuning of the static var compensator (SVC) and power system stabilizers (PSSs) damping controllers. The objective is to improve the damping of low frequency electromechanical oscillations in a multimachine electric power system. In this work, an eigenvalue-based objective function is used for the coordinated tuning of PSSs and SVC controller parameters. To ensure the robustness of the proposed controller, the tuning process takes a large and pre-specified set of operating conditions into account. The effectiveness of the proposed stabilizer is demonstrated through the eigenvalue analysis and nonlinear time-domain simulation. The results of these studies prove that the simultaneous tuning of the TLO based coordinated controller (TLPSS&SVC) has an excellent capability in damping power system low frequency oscillations and enhances greatly the system's dynamic stability. Moreover, it significantly outperforms both the uncoordinated designed stabilizers of the TLO based PSS (TLPSS) and the TLO based SVC (TLSVC).

关键词： static var compensator power system stabilizer multimachine power system coordinated tuning teaching learning based optimization

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TLBO based Droop optimization Technique for DC Microgrid

TLBO based Droop Optimization Technique for DC Microgrid

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IEEE Region 10 Conference on Technology, Knowledge, and Society (TENCON)

作者： Reddy, S. Chakradhar Muthukumari, S. Kumar, T. K. Sunil Kumaravel, S. Kanagalakshmi, S. Natl Inst Technol Elect Engn Dept Calicut Kerala India

ISBN: (纸本)9781728118956

Droop control technique is one of the most commonly used techniques in DC microgrid (MG) to attain proportional load sharing and voltage regulation. Due to the presence of impedance in the transmission line, there are some current sharing errors and voltage deviations across the DC bus. Therefore, loading conditions (LCs) and location of sources in MG have significant effect on current-sharing and voltage regulation. In the present work, droop parameters such as rated voltage and virtual resistance of ith distributed generation (DG) in a MG network are optimized by using optimization procedure by considering all necessary information in order to minimize the impact of line impedances. During the optimization, constraints such as voltage deviations and current sharing errors should be less than five percent of rated voltage and two percent of rated current respectively. By taking combination of voltage regulation and current sharing error, a cost function is formulated. By using load stochastic model, impact of various LCs on the cost functions are weighted. optimization tool called as teaching learning based optimization (TLBO) is then implemented to find out the optimized solution. The proposed algorithm has been illustrated on a low voltage DC MG which comprises of three sources and two loads. Simulation results show effectiveness of proposed method over conventional droop control method in terms of reduced current sharing error and voltage deviations across DC bus.

关键词： Droop control DC microgrid loading conditions teaching learning based optimization

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An efficient multi-unit production planning strategy based on continuous variables

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APPLIED SOFT COMPUTING 2018年 68卷 458-477页

作者： Chauhan, Sandeep Singh Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati 781039 Assam India

Production planning can significantly enhance the competitiveness and hence plays a crucial role in manufacturing industries. In the production planning problem discussed in this article, a set of processes are available which can be operated at various capacity levels with the production and investment costs being proportional to the production of the processes. The determination of the optimal production plan, in terms of the selection of product to be manufactured, the selection and operating capacity of the manufacturing processes in the presence of multiple resource constraints so as to obtain maximum profit is a combinatorial optimization problem. In this article, we state the limitations of the formulation/strategies employed in literature and propose a multi-unit strategy which utilizes only continuous variables to overcome them. The proposed strategy is generic and demonstrated in the context of production planning in a petrochemical industry that has been previously used to potentially guide the petrochemical industries. The proposed strategy is demonstrated with multiple computational intelligence algorithms viz., artificial bee colony, dynamic neighbourhood learning particle swarm optimizer, multi-population ensemble differential evolution, and teaching -learning-based optimization. For the cases discussed in literature, the proposed strategy shows an improvement of up to 12.1% in the profit. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Production planning Artificial bee colony teaching learning based optimization Dynamic neighborhood learning particle swarm optimizer Multi-Population Ensemble Differential Evolution

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New improved metaheuristic approaches for optimum design of posttensioned axially symmetric cylindrical reinforced concrete walls

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2018年第7期27卷 e1461-e1461页

作者： Bekdas, Gebrail Istanbul Univ Dept Civil Engn TR-34320 Istanbul Turkey

Two novel hybrid approaches are presented for optimum design of axially symmetric cylindrical walls subjected to posttensioning loads using metaheuristic algorithms such as harmony search (HS), flower pollination algorithm (FPA), and teaching learning based optimization (TLBO). The objective function of the optimization problem is to minimize the total cost of the wall subjected to constraints on the basis of sectional capacities (bending moment, shear force, and axial tension), ACI 318 (building code requirements for structural concrete) requirements and design variables such as wall thickness, compressive strength of concrete, location and intensities of posttensioning cables, size, and spacing of reinforcement. In the optimum design, the performance of the iterative population based metaheuristic algorithms, HS, FPA, and TLBO are compared and tested by taking wall thickness as discrete and continuous variable. In order to improve the efficiency on finding global optimum results, hybrid forms of the HS combined with FPA and TLBO are effective for the optimization problem.

关键词： axially symmetric cylindrical reinforced concrete walls flower pollination algorithm harmony search optimization optimum design teaching learning based optimization

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Optimal planning of distributed energy resources in harmonics polluted distribution system

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 39卷 99-113页

作者： Kumawat, Manoj Gupta, Nitin Jain, Naveen Bansal, R. C. MNIT Jaipur Dept Elect Engn Jaipur 302017 Rajasthan India MPUAT CTAE Dept Elect Engn Udaipur 313001 India Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa

In this study, harmonics related to power quality issue and energy demand growth are considered simultaneously towards the realistic planning of medium voltage radial distribution system. Mostly, harmonics of distribution networks are produced in the presence of non-linear loads. Therefore, Distributed Energy Resource (DERs) can be placed to mitigate the harmonic distortions and to supply the required system energy demand. This paper presents a Modified Group experience of teaching learning based optimization approach, which can deal with allocation of DERs efficaciously in distorted and non-distorted radial distribution networks. The effectiveness of the proposed approach is validated on standards 33-bus and 69-bus test systems along with 83-bus (Taiwan Power Company) practical radial non-distorted distribution system. The results are compared with already well-established existing methods as suggested in the literature. Further, the proposed algorithm is applied to DER planning considering harmonics generating loads in above-mentioned test systems. The results with linear as well as non-linear loads on all three test systems prove that the proposed strategy can be a robust approach to enhance the system performance towards mitigating increased load demand within the constraints of the distribution system.

关键词： Distributed energy resources Distribution load flow Harmonics spectrum Power quality teaching learning based optimization

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