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International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

作者： Sharma, Sakshi Salgotra, Rohit Singh, Urvinder Thapar Univ Dept Elect & Commun Patiala Punjab India

ISBN: (纸本)9781509032945

Grey wolf optimization (GWO) algorithm is a recent addition to the field of swarm intelligent algorithms. The algorithm is based on the hunting pattern and leadership quality of grey wolfs present in nature. In this paper, to improve the working capabilities of GWO, a new version of GWO namely enhanced GWO (EGWO) has been proposed. The proposed version has been tested on standard benchmark problems to prove its competitiveness with respect to standard state-of-art algorithms. Experimental results show that EGWO is highly competitive and provide better convergence with respect to bat algorithm (BA), flower pollination algorithm (FPA), firefly algorithm (FA), bat flower pollinator (BFP) and GWO. Further convergence profiles validate the superior performance of EGWO.

关键词： grey wolf optimization algorithm bat algorithm flower pollination algorithm numerical optimization benchmark problems

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A novel hybrid model for tourist volume forecasting incorporating search engine data

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ASIA PACIFIC JOURNAL OF TOURISM RESEARCH 2017年第3期22卷 245-254页

作者： Zhang, Binru Huang, Xiankai Li, Nao Law, Rob Univ Int Business & Econ Sch Int Trade & Econ Beijing Peoples R China Sichuan Univ Arts & Sci Dazhou Peoples R China Beijing Union Univ Tourism Coll Beijing Peoples R China Hong Kong Polytech Univ Sch Hotel & Tourism Management Kowloon Hong Kong Peoples R China

The precise prediction of tourism demand has long presented a challenge for both tourism professionals and academics. Tourist volume forecasting is a nonlinear problem, support vector regression (SVR) can approximate a nonlinear system with enough precision, but parameters tuning has always been an obstacle to developing SVR with good generalization potential. Furthermore, previous research mainly used historical observations of tourism demand as the inputs of SVR. This study introduces an approach that hybridizes SVR with the bat algorithm (BA), namely BA-SVR, to forecast tourist volume by incorporating search engine data. In this model, BA is used to adjust the SVR parameters. To validate our proposed approach, tourist volume data for China's Hainan province from August 2008 to October 2015 were used in conjunction with corresponding search engine data as numerical examples. The 12-month simulation forecasts indicate that the BA-SVR is an effective method that can outperform its traditional counterparts.

关键词： Hybrid model support vector regression bat algorithm search engine data tourist volume forecasting

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A Comprehensive Solution for Optimal Capacitor Allocation Problem in Real Distribution Networks 11

A Comprehensive Solution for Optimal Capacitor Allocation Pr...

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2017 International Conference on Electromechanical and Power Systems (SIELMEN)

作者： Neagu, Bogdan Constantin Ivanov, Ovidiu Gavrilas, Mihai Gheorghe Asachi Tech Univ Power Engn Dept Iasi Romania

ISBN: (纸本)9781538618462

The optimal capacitor allocation in electricity distribution networks (EDN) plays a meaningful role in voltage profile improvement, power factor amelioration and also power losses minimization. This work presents two comprehensive optimization algorithms based on metaheuristics as a comparison to solve the capacitor allocation problem in modern distribution networks. Thus, the problem of optimal capacitor allocation for active power losses minimization was solved using the bat and fireworks algorithms. In order to validate and demonstrate the feasibility of the proposed approach, a modern distribution network was tested and the results obtained in MATLAB implementations of the two proposed metaheuristics were compared. The results confirm that the proposed optimization algorithms show a good efficiency and robustness having high performance both for minimizing the EDN power losses and voltage profile improvement.

关键词： capacitor allocation metaheuristic electricity distribution networks fireworks algorithm bat algorithm

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An Optimized Lung Cancer Classification System for Computed Tomography Images 4

An Optimized Lung Cancer Classification System for Computed ...

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4th International Conference on Image Information Processing (ICIIP)

作者： Rattan, Sheenam Kaur, Sumandeep Kansal, Nishu Kaur, Jaspreet GNDEC Dept ECE Ludhiana 141006 Punjab India

ISBN: (纸本)9781509067343

Amongst diverse cancers, lung cancer is measured to be the foremost reason of cancer demise with utmost demise pace. Nodules lying on lungs have distinct structures, they could be either circle or coil shaped which under various circumstances composes the recognition complex. In this work a system has been urbanized for detection of lung cancer in its early stages and classification between malignant and benign tumors via images from Computerized Tomography (CT) scanner. Lung cancer detection process has four steps which include pre-processing phase, segmentation, feature extraction and lung cancer cell classification. bat algorithm is applied to provide considerable optimization results which improve the performance of system. The classification between malignant nodules and benign has been done through Artificial Neural Network Ensemble to provide results of higher accuracy. The overall accuracy, sensitivity and specificity of 98.5%, 100% and 91% respectively is acquired in the system.

关键词： Lung cancer CT Images Watershed Transformation bat algorithm Graylevel co-occurrence matrix (GLCM) Artificial Neural Network Ensemble (ANNE)

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New Teaching-Learnig Based Optimization algotithm with Random Local Search : TLBO-RLS 5

New Teaching-Learnig Based Optimization algotithm with Rando...

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5th International Conference on Electrical Engineering - Boumerdes (ICEE-B)

作者： Bourahla, Kheireddine Belli, Zoubida Hacib, Tarik Achoui, Imad Jijel Univ Dept Elect Engn Ouled Aissa BP 98 Jijel 1800 Algeria

ISBN: (纸本)9781538606865

In this work a new improved version of Teatching Learnning Based Optimization algorithm, TLBO, is proposed. The new strategy is obtained by tne inclusion of the bat algorithm, BA, random local search part in the optimization process with TLBO algorithm. The developped hybrid algorihm is applied jointly with 2D non-linear finite elment method to solv the Team workshop problem 25.

关键词： bat algorithm Teaching-Learning Based Optimization algorithm team workshop problem 25

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Evaluation of By-Pass Diode and DMPPT under Partial Shade Condition of Photovoltaic Systems 7

Evaluation of By-Pass Diode and DMPPT under Partial Shade Co...

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7th International Conference on Power Systems (ICPS)

作者： Yetayew, Tefera T. Jyothsna, T. R. Kusuma, G. Andhra Univ Dept Elect Engn Coll Engn A Visakhapatnam 530003 Andhra Prades India

ISBN: (纸本)9781538617892

Partial shade and mismatching among PV modules in a PV system are common phenomenon caused by trees, passing clouds, manufacturing tolerances, etc. Due to mismatching/partial shading, the P-V characteristic curve usually comprises of multiple local and a global peak. To extract the maximum energy, usually three techniques are proposed. The first one and widely used is to use central MPPT technique (use of by-pass diodes) to track the global peak, the second is use of distributed per module/sub-module MPPT (DMPPT) and the third is reconfiguration of modules in an array. The second and third options can increase the extracted PV power. However, the increase in cost and implementation complexity and control are some of the challenges of the techniques. In this paper, performance evaluation of by-pass diode and DMPPT per module technique for PV system under partial shade has been done.

关键词： bat algorithm by-pass diode DMPPT partial shading photovoltaic power System

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An Improved Firefly algorithm For Numerical Optimization

An Improved Firefly Algorithm For Numerical Optimization

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International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

作者： Kaur, Komalpreet Salgotra, Rohit Singh, Urvinder Thapar Univ Dept Elect & Commun Patiala Punjab India

ISBN: (纸本)9781509032945

Firefly algorithm (FA) is a recently introduced algorithm based upon the flashing light pattern of fireflies. This algorithm has proved its significance for various optimization problems but has the problem of trapping in local optima. In this article, in order to, improve the performance of FA, a new improved FA (IFA) has been proposed. The proposed algorithm has been applied to standard state-of-the-art algorithms and it has been found experimentally that WA provide improved performance with respect to the standard FA, differential evolution (DE), bat algorithm (BA) and flower pollination algorithm (FPA).

关键词： Firefly algorithm differential evolution bat algorithm benchmark problems global optimization

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Solving Two-Dimensional Rectangle Packing Problem Using Nature-Inspired Metaheuristic algorithms

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JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT-INNOVATION AND ENTREPRENEURSHIP 2018年第2期3卷

作者： Virk, Amandeep Kaur Singh, Kawaljeet Punjabi Univ Univ Coll Engn Patiala Punjab India Punjabi Univ Patiala Punjab India

This paper applies cuckoo search and bat metaheuristic algorithms to solve two-dimensional non-guillotine rectangle packing problem. These algorithms have not been found to be used before in the literature to solve this important industrial problem. The purpose of this work is to explore the potential of these new metaheuristic methods and to check whether they can contribute in enhancing the performance of this problem. Standard benchmark test data has been used to solve the problem. The performance of these algorithms was measured and compared with genetic algorithm and tabu search techniques which can be found to be used widely in the literature to solve this problem. Good optimal solutions were obtained from all the techniques and the new metaheuristic algorithms performed better than genetic algorithm and tabu search. It was seen that cuckoo search algorithm excels in performance as compared to the other techniques.

关键词： Rectangle packing non-guillotine cutting cuckoo search bat algorithm

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Hybrid bat and Levenberg-Marquardt algorithms for Artificial Neural Networks Learning

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2016年第5期32卷 1301-1324页

作者： Nawi, Nazri Mohd Rehman, Muhammad Zubair Khan, Abdullah Kiyani, Arslan Chiroma, Haruna Herawan, Tutut Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Software & Multimedia Ctr Johor Baharu 86400 Malaysia Univ Malaya Fac Comp Sci & Informat Technol Lumpur 50603 Malaysia Univ Teknol Yogyakarta Yogyakarta Indonesia AMCS Res Ctr Yogyakarta Indonesia

The Levenberg-Marquardt (LM) gradient descent algorithm is used extensively for the training of Artificial Neural Networks (ANN) in the literature, despite its limitations, such as susceptibility to the local minima that undermine its robustness. In this paper, a bio-inspired algorithm referring to the bat algorithm was proposed for training the ANN, to deviate from the limitations of the LM. The proposed bat algorithm-based LM (BALM) was simulated on 10 benchmark datasets. For evaluation of the proposed BALM, comparative simulation experiments were conducted. The experimental results indicated that the BALM was found to deviate from the limitations of the LM to advance the accuracy and convergence speed of the ANN. Also, the BALM performs better than the back-propagation algorithm, artificial bee colony trained back-propagation ANN, and artificial bee colony trained LM ANN. The results of this research provide an alternative ANN training algorithm that can be used by researchers and industries to solve complex real-world problems across numerous domains of applications.

关键词： bat algorithm Levenberg-Marquardt algorithm artificial neural networks optimization swarm intelligence

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A bat-inspired approach to define transition rules for a cellular automaton model used to simulate urban expansion

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INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 2016年第10期30卷 1961-1979页

作者： Cao, Min Bennett, Sean J. Shen, Quanfei Xu, Ruqi Nanjing Normal Univ Minist Educ Key Lab Virtual Geog Environm Nanjing Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing Jiangsu Peoples R China Univ Buffalo Dept Geog Buffalo NY USA Prov Fundamental Geomat Ctr Jiangsu Nanjing Jiangsu Peoples R China

A new metaheuristic approach is presented to discover transition rules for a cellular automaton (CA) model using a novel bat movement algorithm (BA). CA is capable of simulating the evolution of complex geographical phenomena, and transition rules lie at the core of these models. An intelligence algorithm based on the echolocation behavior of bats is used to discover explicit transition rules for use in simulating urban expansion. CA transition rules are formed by links between attribute constraint items and classification items. The transition rules are derived using the BA to optimize the lower and upper threshold values for each attribute. The BA-CA model is then constructed for the simulation of urban expansion observed for Nanjing City, China. The total accuracy of newly formulated BA-CA model for this application is 86.9%, and the kappa coefficient is 0.736, which strongly suggest that the interactions of bats are effective in capturing the relationships between spatial variables and urban dynamics. It is further demonstrated that this bat-inspired BA-CA model performs better than the null model, the particle swarm optimization-based CA model (PSO-CA), and the ant colony optimization-based CA model (ACO-CA) using the same dataset. The model validation and comparison illustrate the novel capability of BA for discovering transition rules of CA during the simulation of urban expansion and potentially for other geographic phenomena.

关键词： bat algorithm swarm intelligence transition rules cellular automata urban expansion

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