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Collaborative optimization algorithm for learning path construction in E-learning

COMPUTERS & ELECTRICAL ENGINEERING 2019年 77卷 325-338页

作者： Vanitha, V. Krishnan, P. Elakkiya, R. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Comp Sci & Engn Chennai 600062 Tamil Nadu India ICAR Natl Acad Agr Res Management Hyderabad 500030 India SASTRA Deemed Univ Sch Comp Thanjavur 613401 India

In e-learning, learning object sequencing is a challenging task. It is difficult to sequence learning objects manually due to their abundant availability and the numerous combinations possible. An adaptive e-learning system that offers a personalized learning path would enhance the academic performance of learners. The main challenge in providing a personalized learning path is finding the right match between individual characteristics and learning content sequences. This paper presents a collaborative optimization algorithm, combining ant colony optimization and a genetic algorithm to provide learners with a personalized learning path. The proposed algorithm utilizes the stochastic nature of ant colony optimization and exploration characteristics of the genetic algorithm to build an optimal solution. Performance of the proposed algorithm has been assessed by conducting qualitative and quantitative experiments. This study establishes that the hybrid approach provides a better solution than the traditional approach. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Genetic algorithm ACO hybrid optimization algorithm Learning path Learning object sequence E-learning

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A novel hybrid method combining electromagnetism-like mechanism and firefly algorithms for constrained design optimization of discrete truss structures

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COMPUTERS & STRUCTURES 2019年第Feb.期212卷 20-42页

作者： Duc Thang Le Dac-Khuong Bui Tuan Duc Ngo Quoc-Hung Nguyen Nguyen-Xuan, H. Vietnamese German Univ Dept Computat Engn Binh Duong Vietnam Univ Melbourne Dept Infrastruct Engn Melbourne Vic 3010 Australia Ho Chi Minh City Univ Technol HUTECH CIRTeh Inst Ho Chi Minh City Vietnam Sejong Univ Dept Architectural Engn 209 Neungdong Ro Seoul 05006 South Korea

A new optimization method called Electromagnetism-like Firefly algorithm (EFA), which is a novel hybrid between the Electromagnetism-like algorithm (EM) and the Firefly algorithm (FA) for discrete structural optimization is proposed. The EFA inherits the advantages of both the FA and the EM. This proposed optimization algorithm is then presented to improve both solution accuracy and convergence speed, as well as to treat constrained optimization problems with discrete design variables. In EFA, modified formulas of interactive forces are used to increase the diversification of the population, and the constraint violations are embedded into the charges of all electromagnetic fireflies to avoid becoming trapped in unfeasible domains. A mechanism called "current-to-best" electromagnetic movement is incorporated with traditional interactive movements to balance the exploration and the exploitation abilities of the EFA. In the local search phase, a newly bi-directional searching procedure is performed on the best firefly to intensify effectively its local optimum. In order to guarantee the convergence capability of the EFA, a harmonized selection mechanism combined with the traditional and elitist selections is proposed and applied if the algorithm cannot find a better optimal solution during a number of predetermined optimization loops. In addition, in this study, the FA and EM with some improvements in several phases are also proposed to solve the constrained optimization problems. Finally, a rounding technique is applied to the proposed EFA, FA and EM for solving discrete optimization problems. The improved performance of the EFA in comparison with the FA, EM as well as other optimization algorithms in the literature is demonstrated by six popular truss optimization problems with discrete variables. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： hybrid optimization algorithm Electromagnetism-like firefly algorithm (EFA) Firefly algorithm (FA) Electromagnetism-like mechanism algorithm (EM) Discrete constrained optimization Truss structures

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Solution of economic dispatch problem for wind-thermal power systems by a modified hybrid optimization method

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2019年第4期34卷 1871-1895页

作者： Tefek, Mehmet Fatih Uguz, Harun Kirsehir Ahi Evran Univ Kaman Vocat Sch Dept Comp Technol TR-40300 Kaman Kirsehir Turkey Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn TR-42250 Konya Turkey

Economic Dispatch Problem (EDP) is a complex, constrained and non-linear optimization problem. In the EDP, it is aimed to minimize the system fuel cost between minimum and maximum limits of the active power buses. In this study, a modified hybrid Gravitational Search- Teaching-Learning Based optimization algorithm (MHGT), a quick, efficient and reliable method is proposed by combining standard Gravitational Search algorithm (GSA) and Teaching-Learning Based optimization (TLBO). The proposed MHGT method was developed by modifying the global search superiority in GSA and powerful local search specialty in TLBO for the solution of constrained optimization problem. The MHGT was tested experimentally by well-known and mostly used ten benchmark function in the literature. The proposed method was first implemented on a 6 bus wind-thermal power system for 400, 450 and 500 MW powers. Then, it was implemented on Turkey 19 bus wind-thermal power system according to different ratios of the installed power as 25, 27.5 and 30 percent to solve the EDP problem. The obtained results were compared with the results of other studies. From the results, it is seen that the proposed MHGT method finds the solution in a short execution time and less fuel cost with more reliably and more efficiently in terms of both fuel cost and execution time.

关键词： hybrid optimization algorithm gravitational search algorithm teaching-learning based algorithm economic dispatch problem wind-thermal power system

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A combination of FA and SRPSO algorithm for Combined Heat and Power Economic Dispatch

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APPLIED SOFT COMPUTING 2021年 102卷 107088-107088页

作者： Nasir, Mohammad Sadollah, Ali Aydilek, Ibrahim Berkan Ara, Afshin Lashkar Nabavi-Niaki, Seyed Ali Islamic Azad Univ Mat & Energy Res Ctr Dezful Branch Dezful Iran Univ Sci & Culture Dept Mech Engn Tehran Iran Harran Univ Fac Engn Dept Comp Engn Sanliurfa Turkey Islamic Azad Univ Dept Elect Engn Dezful Branch Dezful Iran Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

This paper represents a hybrid Firefly and Self-Regulating Particle Swarm optimization (FSRPSO) algorithm to solve optimal Combined Heat and Power Economic Dispatch (CHPED) problem. Valve point effect on fuel cost function of pure generation units, electrical power losses in transmission systems and feasible operating zones are taken into account in the CHPED problem. The CHPED refers to minimize total costs of fuel for electricity and heat generation supply to load demand. The proposed FSRPSO attempts to determine the start of the local search process properly by checking the previous global best. Thus, the FSRPSO is able to exploit strong points of both Firefly algorithm (FA) and SRPSO mechanisms in order to balance between exploration and exploitation phases. Besides, for the sake of validation the proposed hybrid method, the FSRPSO is examined on 21 well-known benchmarks, and also a real engineering case i.e., two power systems for evaluating its performance compared with the SRPSO, FA, PSO, and other state-of-the-art algorithms. The obtained optimization results show that the proposed FSRPSO provides fast, mature and reliable optimum solutions and outperform other compared algorithms in diverse categories of benchmarks along with the studied CHPED problem. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Firefly Self-Regulating Particle Swarm optimization Combined Heat and Power Economic Dispatch hybrid optimization algorithm Metaheuristics

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Optimum Sanitary Sewer Network Design Using Shuffled Gray Wolf Optimizer

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JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE 2021年第4期12卷 04021055-04021055页

作者： Masoumi, Fariborz Masoumzadeh, Sina Zafari, Negin Javad Emami-Skardi, Mohammad Univ Mohaghegh Ardabili Dept Civil Engn Daneshgah St Ardebil *** Iran Univ Tabriz Fac Civil Engn 29 Bahman Blvd Tabriz *** Iran Univ Tehran Sch Civil Engn Coll Engn Tehran *** Iran

Sanitary sewer networks are among the most important and most costly infrastructures in the water resources management field. However, the optimum design of these networks is cumbersome and sophisticated;due to these systems' sensitive and costly construction, the application of optimization algorithms is essential. Focusing on solving this nonlinear optimization problem with continuous and discrete constraints, the shuffled gray wolf optimizer (SGWO), which is a hybrid algorithm inspired by shuffled complex evolution (SCE) and gray wolf optimizer (GWO), was used to solve two hypothetical networks and one real sewer network with different scales and geometries and was compared to four well-known metaheuristic optimization algorithms. The results showed that the population division in the SGWO algorithm not only improved the results obtained by the GWO and other algorithms but also the costs were lower than those achieved using other famous optimization algorithms. This happened while the SGWO algorithm used a considerably smaller number of function evaluations. Moreover, this algorithm exhibited low standard deviation and average objective function value in 20 independent runs, which shows this algorithm's reliability.

关键词： Gray wolf optimization (GWO) Shuffled complex evolution (SCE) Sewer design optimization Metaheuristic algorithms hybrid optimization algorithm

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Inversion of bottom parameters using a backscattering model based on the effective density fluid approximation

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APPLIED ACOUSTICS 2021年 182卷 108187-108187页

作者： Yu, Shengqi Liu, Baohua Yu, Kaiben Yang, Zhiguo Kan, Guangming Zong, Le Minist Nat Resources Natl Deep Sea Ctr Qingdao 266237 Peoples R China Pilot Natl Lab Marine Sci & Technol Qingdao Lab Marine Geol & Environm Qingdao 266237 Peoples R China First Inst Oceanog Key Lab Marine Sedimentol & Environm Geol Qingdao 266061 Peoples R China

An inversion method using backscattering strength is used to obtain bottom parameters. A backscattering model based on the effective density fluid approximation was established as the forward model. The objective function used to scale the mismatch between the model predictions and the measured data (obtained by employing two monostatic transducers with narrow directivity) was established on the assumption that the data errors follow a Gaussian distribution. The maximum a posteriori estimations of inversion parameters were obtained by using a two-step hybrid optimization algorithm combining the differential evolution algorithm and the particle swarm optimization algorithm. The uncertainty and the correlation of inversion parameters were presented using marginal probability distributions and a covariance matrix through Bayesian inversion. Using this method, we can acquire not only geoacoustic parameters (including sound speed and attenuation) but also partial physical parameters of marine sediments and statistical character parameters of seafloor roughness and sediment heterogeneity. Finally, the efficiency of this inversion method was verified through comparison between inversion results and sampling data from sand sediment at the bottom of a water tank. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Acoustic scattering Bottom parameters hybrid optimization algorithm Bayesian inversion

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Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer

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APPLIED ENERGY 2021年 304卷 117707-117707页

作者： Zou, Runmin Yang, Jiaxin Wang, Yun Liu, Fang Essaaidi, Mohamed Srinivasan, Dipti Cent South Univ Sch Automat Changsha Hunan Peoples R China Mohamed V Univ Smart Syst Lab SSL ENSIAS Rabat Morocco Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore

Wind energy is one of the most promising solutions to energy crisis and environmental pollution, so it is being developed rapidly. Wind turbine power curves (WTPCs) play an important role in wind energy assessment, turbine condition monitoring, and power grid dispatching. However, there are two challenges in WTPC modeling: model selection and parameter optimization. Many parametric and non-parametric models have been developed to characterize WTPCs, but none can always perform the best due to the complex wind regimes. In this paper, considering the simple structure and interpretability of model parameters, a set of parametric WTPC models is constructed to adapt to the variability of wind regimes, and the optimal candidate will be selected from the set according to their performances. As to the process of parameter optimization, a novel loss function, which considers the asymmetric error characteristic of WTPC modeling, is proposed, and a hybrid intelligent optimization method named GWO-BSA, which makes full use of the advantages of grey wolf optimizer and backtracking search algorithm, is designed. Finally, a novel WTPC modeling strategy, which combines the candidate model set, error characteristic-based loss function, and GWO-BSA, is proposed to obtain better power curves. Experimental results show that (1) GWO-BSA shows faster convergence speed and higher optimization accuracy than single optimization algorithms;(2) the proposed error characteristic-based loss function has better performance than the commonly used symmetric loss functions;and (3) compared with some popular artificial intelligence-based models, the designed WTPC modeling strategy produces better WTPCs under different wind regimes.

关键词： Wind turbine power curve modeling Asymmetric error characteristics Error characteristic-based loss function hybrid optimization algorithm

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Study on the distributed model predictive control for multi-zone buildings in personalized heating

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ENERGY AND BUILDINGS 2021年 231卷 110627-110627页

作者： Li, Zhiwei Zhang, Jili Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Peoples R China

To meet demands of smart heating development, the novel dynamic regulation methods of heating systems need to be pursued. This paper presents a model predictive control (MPC) approach for room temperature regulation in multi-zone buildings. Among them, a) For heating systems with nonlinearity and large time delay, a hybrid optimization algorithm consisting of the improved particle swarm optimization (IPSO) algorithm and Newton-Raphson (NR) method in online optimization of MPC for a single zone building is proposed. Compared with the quadratic programming method, it can identify successfully the optimal control vector. b) A distributed MPC approach is presented for multi-zone buildings existing hydraulic coupling. Firstly, Nash-optimization acts on searching the global equilibrium point in the distributed structure, then the hybrid optimization algorithm works in the online optimization of these local MPCs. Compared with the decentralized MPC, the control performance is improved through co-operating and communicating between subsystems via network to share information related to their future behavior. Besides, the global performance index in the distributed MPC is redefined as the weighted sum of energy consumption and thermal comfort, which provides the possibility to eliminate quickly the most unfavorable thermodynamic loop in heating systems and reduce energy consumption. (C) 2020 Published by Elsevier B.V.

关键词： Personalized heating Distributed model predictive control hybrid optimization algorithm Nash-optimization Energy saving

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A P systems based hybrid optimization algorithm for parameter estimation of FCCU reactor-regenerator model

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CHEMICAL ENGINEERING JOURNAL 2012年 211卷 508-518页

作者： Yang, Shipin Wang, Ning Zhejiang Univ Natl Key Lab Ind Control Technol Inst Cyber Syst & Control Hangzhou 310027 Zhejiang Peoples R China

Fluid catalytic cracking unit (FCCU) reactor-regenerator is a complex multivariable system and suitable model is useful to understand and design FCCU reaction-regeneration processes. Inspired by the concepts of P systems (membrane computing) and quantum computing, a P systems based hybrid optimization algorithm (PHOA) is proposed to estimate the parameters of FCCU reactor-regenerator model. The expansion of the search space makes the novel algorithm realize exploration and the dynamic contraction of the search space makes it realize exploitation. The quantum update rules and adaptive mutation rule in combination with the new membrane structure contribute to the improvement of global searching performance. Studies on some benchmark functions indicate that the PHOA outperforms DNA based genetic algorithm (DNA-GA) and genetic algorithm (GA) both in search efficiency and accuracy. With the parameters obtained by PHOA, the transfer function matrix model of FCCU reactor-regenerator in an oil refinery is established. Experimental results reveal that model curves gained by this proposed method are in good agreement with the measured data with smaller sum of squared error. The effectiveness of the model is also validated by experiments. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Parameter estimation P systems hybrid optimization algorithm Reactor-regenerator kinetic model Fluid catalytic cracking unit (FCCU)

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A new modeling and solution method for optimal energy flow in electricity-gas integrated energy system

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2019年第9期43卷 4322-4343页

作者： Tan, Yuhua Wang, Xin Zheng, Yihui Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

With the increasing interdependency of electricity and gas, it is necessary to simultaneously investigate electric power system and natural gas system from the perspective of an electricity-gas integrated energy system (EGIES). As an extension and integration of both optimal power flow (OPF) and optimal gas flow (OGF), optimal energy flow (OEF) is regarded as the cornerstone of the EGIES and lays an essential foundation for further research on the EGIES's operation and analysis considering stochastic conditions and contingency states. The objective of this paper is to develop a generalized mathematical model and a universally applicable simulation tool for the OEF problem. First, natural gas system is modeled in a way similar to electric power system according to electricity-gas analogy analysis, where gas admittance, gas nodal admittance matrix, and the nodal equation of gas flow conservation are derived. Then, a generalized accurate OEF model is formulated by simultaneously integrating the OPF model and the OGF model as well as their coupling constraints in a unified modeling framework. Furthermore, an available hybrid optimization approach consisting of whale optimization algorithm, MATPOWER, hydraulic calculation iterative program, and nonstationary penalty function method is put forward to solve the OEF problem. The accuracy, feasibility, and applicability of the proposed modeling and solution method is finally demonstrated by analyzing Belgian 20-node gas system combined with IEEE 30-bus test system.

关键词： electric power system hybrid optimization algorithm integrated energy system natural gas system optimal energy flow optimal power flow optimal gas flow

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