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A general model for energy hub economic dispatch

APPLIED ENERGY 2017年 190卷 1090-1111页

作者： Beigvand, Soheil Derafshi Abdi, Hamdi La Scala, Massimo Razi Univ Fac Engn Dept Elect Engn Kermanshah Iran Politecn Bari Polytech Sch Bari Elect & Elect Dept DEE Bari Italy

This paper proposes a new optimization algorithm, namely Self-Adoptive Learning with Time Varying Acceleration Coefficient-gravitational search algorithm (SAL-TVAC-GSA), to solve highly nonlinear, non-convex, non-smooth, non-differential, and high-dimension single- and multi-objective Energy Hub Economic Dispatch (EHED) problems. The presented algorithm is based on GSA considering three fundamental modifications to improve the quality solution and performance of original GSA. Moreover, a new optimization framework for economic dispatch is adapted to a system of energy hubs considering different hub structures, various energy carriers (electricity, gas, heat, cool, and compressed air), valve-point loading effect and prohibited zones of electric-only units, as well as the different equality and inequality constraints. To show the effectiveness of the suggested method, a high-complex energy hub system consisting of 39 hubs with 29 structures and 76 energy (electricity, gas, and heat) production units is proposed. Two individual objectives including energy cost and hub losses are minimized separately as two single-objective EHED problems. These objectives are simultaneously minimized in the multi objective optimization. Results obtained by SAL-TVAC-GSA in terms of quality solution and computational performance are compared with Enhanced GSA (EGSA), GSA, Particle Swarm Optimization (PSO), and Genetic algorithm (GA) to demonstrate the ability of the proposed algorithm in finding an operating point with lower objective function. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch Energy hub Energy hub economic dispatch gravitational search algorithm Self-adoptive learning with time varying acceleration coefficient-gravitational search algorithm Optimization

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A niche GSA method with nearest neighbor scheme for multimodal optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2017年 35卷 78-92页

作者： Haghbayan, Pourya Nezamabadi-Pour, Hossein Kamyab, Shima Shahid Bahonar Univ Kerman Dept Elect Engn POB 76169-133 Kerman Iran

In this paper, a new niching method based on gravitational search algorithm (GSA) is proposed in which species are formed within the population (swarm) based on a nearest neighbor (NN) scheme. Also, we suggest a scheme to detect the niches inside the population by using the hill valley algorithm without the need of a pairwise comparison between any pair of solutions inside the population. In order to improve the exploitation capability of the proposed niching method, the formed species are balanced such that they are forced to have almost equal number of members. This mechanism enables the species to explore more optima via diversity conservation in the swarm. Experimental results of using several multimodal benchmark functions confirm the effectiveness of the proposed niching scheme compared to well-known existing niching methods.

关键词： Multimodal optimization Swarm intelligence gravitational search algorithm Nearest neighbor scheme Hill-valley algorithm

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Optimal operational efficiency of chillers using oil-free centrifugal compressors

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INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID 2017年 82卷 83-96页

作者： Lissandrin, Marco Rampazzo, Mirco Cecchinato, Luca Beghi, Alessandro Everest Srl Via Roveredo 20 I-33170 Pordenone Italy Univ Padua Dipartimento Ingn Informaz Via Gradenigo 6-B I-35131 Padua Italy

In chiller design, oil-free variable-speed centrifugal compressors are becoming increasingly popular. However, the management of systems equipped with this kind of compressor is a non-trivial task. This work focuses on the efficient operation of variable-speed air-condensed chillers with variable-speed centrifugal compressors paired with (oil-free) magnetic bearings. Multiple operating conditions, at any moment in time, together with wide cooling ranges and potentially high energy efficiencies during off-peak demands create the need for an open-loop energy optimisation strategy via an efficiency-based fitness function. The physical variables of this function and their constraints are discussed including several variable dependencies. The problem of devising strategies for improving chillers' efficiency is here formulated as a model-based optimisation and it is solved by means of an ad hoc hybrid algorithm which combines a deterministic method and stochastic one. The results of simulations, which are based on two chiller layouts, show the potential of the proposed approach. (c) 2017 Elsevier Ltd and IIR. All rights reserved.

关键词： Chiller Centrifugal compressor Magnetic bearings Energy optimisation gravitational search algorithm

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Metaheuristic search algorithms in Solving the n-Similarity Problem

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FUNDAMENTA INFORMATICAE 2017年第2期152卷 145-166页

作者： Mirhosseini, Mina Nezamabadi-pour, Hossein Higher Educ Complex Bam Dept Comp Sci Bam Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

The - similarity problem, finding a group of objects which have the most similarity to each other, has become an important issue in information retrieval and data mining. The theory of this concept is mathematically proven, but it practically has high time complexity. Binary Genetic algorithm (BGA) has been applied to improve solutions quality of this problem, but a more efficient algorithm is required. Therefore, we aim to study and compare the performance of four metaheuristic algorithms called Particle Swarm Optimization (PSO), gravitational search algorithm (GSA), Imperialist Competitive algorithm (ICA) and Fuzzy Imperialist Competitive algorithm (FICA) to tackle this problem. The experiments are conducted on two applications;the former is on four UCI datasets as a general application and the latter is on the text resemblance application to detect multiple similar text documents from Reuters datasets as a case study. The results of experiments give a ranking of the algorithms in solving the - similarity problem in both applications based on the exploration and exploitation abilities, that the FICA achieves the first rank in both applications as well as based on the both criteria.

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Design optimization of wireless sensor networks in precision agriculture using improved BQIGSA

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2017年 16卷 38-47页

作者： Mirhosseini, Mina Barani, Fatemeh Nezamabadi-pour, Hossein Higher Educ Complex Bam Dept Comp Sci Bam Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

Using Wireless Sensor Networks (WSNs) is a new direction of research in agricultural and farming applications. Recently, WSNs are widely used in precision agriculture. One of the most important issues in WSNs is improving the energy consumption of the network while the special properties and the connectivity requirements are considered. In this study, a modified binary version of quantum-inspired gravitational search algorithm is developed to optimize the performance of the WSN used in precision agriculture. The proposed approach is applied to a WSN composed of sensor deployed in a farm to monitor the environmental conditions for a precision agriculture. The aim is to determine the operational mode of each sensor to improve the energy consumption and extend the life span of the network, taking into consideration the communication and application-specific requirements. To demonstrate the benefit of our proposed approach, which is called improved BQIGSA, a comparison between this approach and the existing methods including Binary Genetic algorithm (BGA), Binary Particle Swarm Optimization (BPSO), the Quadrivalent Quantum-Inspired gravitational search algorithm (QQIGSA) and the Binary Quantum-Inspired gravitational search algorithm (BQIGSA) is given. The results of the performed experiments indicate the effectiveness of the WSN designed by the proposed modified BQIGSA in improving the energy consumption and subsequently prolonging the life span of the network. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Wireless Sensor Network Energy Consumption Precision Agriculture Quantum Computing gravitational search algorithm Binary Quantum-Inspired gravitational search algorithm

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Unit commitment by an improved binary quantum GSA

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APPLIED SOFT COMPUTING 2017年 60卷 180-189页

作者： Barani, Fatemeh Mirhosseini, Mina Nezamabadi-pour, Hossein Farsangi, Malihe M. Higher Educ Complex Bam Dept Comp Sci Bam Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

Unit commitment (UC) problem is an important optimizing task for scheduling the on/off states of generating units in power system operation over a time horizon such that the power generation cost is minimized. Since, increasing the number of generating units makes it difficult to solve in practice, many approaches have been introduced to solve the UC problem. This paper introduces an improved version of the binary quantum-inspired gravitational search algorithm (BQIGSA) and proposes a new approach to solve the UC problem based on the improved BQIGSA, called QGSA-UC. The proposed approach is applied to unit commitment problems with the number of generating units in the range of 10120 along with 24-h scheduling horizon and is compared with nine state-of-the-art approaches. Furthermore, four different versions of gravitational approach are implemented for solving the UC problem and compared with those obtained by QGSA-UC. Comparative results clearly reveal the effectiveness of the proposed approach and show that it can be used as a reliable tool to solve UC problem. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Unit commitment problem Economic dispatch Quantum computing gravitational search algorithm

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Optimized controllers for enhancing dynamic performance of PV interface system

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Journal of Electrical Systems and Information Technology 2018年第1期5卷 1-10页

作者： Mahmoud A. Attia Department of Electric Power and Machines Faculty of Engineering Ain Shams University Egypt

The dynamic performance of PV interface system can be improved by optimizing the gains of the Proportional–Integral (PI) controller. In this work, gravitational search algorithm and harmony search algorithm are utilized to optimal tuning of PI controller gains. Performance comparison between the PV system with optimized PI gains utilizing different techniques are carried out. Finally, the dynamic behavior of the system is studied under hypothetical sudden variations in irradiance. The examination of the proposed techniques for optimal tuning of PI gains is conducted using MATLAB/SIMULINK software package. The main contribution of this work is investigating the dynamic performance of PV interfacing system with application of gravitational search algorithm and harmony search algorithm for optimal PI parameters tuning.

关键词： Photovoltaic power systems gravitational search algorithm Harmony search algorithm Genetic algorithm Artificial intelligence

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Image recognition algorithm based on Parameter Optimization of gravitational search

Image recognition algorithm based on Parameter Optimization ...

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2016 2nd International Conference on Electronics,Network and Computer Engineering(ICENCE 2016)

作者： LEI Hu Xi’an Fanyi University

Image characteristic value extracted by gravitational search algorithm improves the image retrieval accuracy. The distance between the image characteristic values is as the input value, accuracy of image recognition is calculated by pulse coupled network variates optimized of gravitational search algorithm. The retrieval accuracy of pulse coupled network optimized by gravitational search algorithm improves by 7% at least than method of relevance feedback, color edge combined discrete wavelet transform.

关键词： gravitational search algorithm Parameter optimization Image recognition

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QQIGSA: A quadrivalent quantum-inspired GSA and its application in optimal adaptive design of wireless sensor networks

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2017年第Jan.期78卷 231-241页

作者： Mirhosseini, Mina Barani, Fatemeh Nezamabadi-pour, Hossein Dept Comp Sci Higher Educ Complex Barn Bam Iran Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

In the recent past, wireless sensor networks (WSN) have been used in a wide variety of applications which have special properties and requirements. In this study, a novel version of quantum-inspired gravitational search algorithm is presented as a new optimization methodology which is well suitable for quadrivalent encoded problems. The proposed method is adapted in a wireless sensor network in precise agriculture application to find an optimal and adaptive design of wireless sensor network to improve the energy consumption and extend the life span of the network, taking into consideration the communication limitations and application-specific requirements. The performance of the proposed method which is called QQIGSA is compared with the binary genetic algorithm (BGA) and binary particle optimization (BPSO). The experimental results indicate that the WSN designed by QQIGSA has the most performance in fulfilling the requirements and prolonging the lifetime of the network.

关键词： Wireless sensor network Quantum computing gravitational search algorithm Quadrivalent quantum-inspired gravitational search algorithm

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A hybrid intelligent scheme for estimating band gap of doped titanium dioxide semiconductor using crystal lattice distortion

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COMPUTATIONAL MATERIALS SCIENCE 2017年 137卷 249-256页

作者： Owolabi, Taoreed O. Gondal, Mohammed A. King Fahd Univ Petr & Minerals Ctr Excellence Nanotechnol Dept Phys Laser Res Grp Box 5047 Dhahran 31261 Saudi Arabia

Wide band gap of titanium dioxide (TiO2) semiconductor remains a challenge in photo-catalysis application where light absorption ability of the semiconductor is desired to go beyond ultra-violent region without enhancement of charge recombination rate. In order to address this challenge, two-layer feed-forward neural network is proposed for doped titanium dioxide band gap estimation using sensitivity based linear learning method (SBLLM) hybridized with gravitational search algorithm (GSA) for efficient optimization of the number of epoch and hidden neurons. The performance sensitivity of the proposed hybrid SBLLM-GSA model on the gravitational constant and the number of agents are simulated and discussed. The hybrid SBLLM-GSA model outperforms ordinary SBLLM with performance improvement of 13.13% on the basis of root mean square error. The ability of the proposed SBLLM-GSA model to generalize to unseen data was assessed by feeding the model with the crystal lattice parameters of indium doped TiO2, copper indium co-doped TiO2 as well as sulfur doped TiO2 semiconductor and the obtained band gaps agree well with the measured values. The outcomes of this work indicate the effectiveness of the proposed hybrid model in estimating the band gap of TiO2 semiconductor for efficiency and performance improvement in photo-catalysis application as well as other applications where band gap adjustment is essential for performance enhancement of the semiconductor. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Sensitivity based linear learning method Band gap gravitational search algorithm Lattice parameters Titanium dioxide semiconductor and neural network

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