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Universal Generalized Net Model for Description of Metaheuristic algorithms: Verification with the bat algorithm 4th

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10th Conference of the European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT) / 16th International Workshop on Intuitionistic Fuzzy Sets and Generalized Nets (IWIFSGN)

作者： Roeva, Olympia Atanassova, Vassia Bulgarian Acad Sci Inst Biophys & Biomed Engn Bioinformat & Math Modelling Dept 105 Acad G Bonchev Str BU-1113 Sofia Bulgaria

ISBN: (纸本)9783319668277;9783319668260

In the present paper, the apparatus of generalized nets is used to describe the metaheuristic technique bat algorithm. Generalized nets are considered an effective and appropriate tool for description of the logics of different optimization techniques. As a result, the developed generalized net model executes the bat algorithm procedures, conducting basic steps and performing optimal search. The paper elaborates on the already proposed Universal generalized net model for description of the population-based metaheuristic algorithms, which was used so far to model the Cuckoo search, Firefly algorithm and Artificial bee colony optimization, and is used here for modelling of bat algorithm. It is shown that the bat algorithm can be described in terms of Universal generalized net model by only varying the characteristic functions of the tokens. Thus, verification of the Universal generalized net model is performed.

关键词： Generalized nets Modelling Metaheuristic bat algorithm

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Steepest Descent bat algorithm for Solving Systems of Non-linear Equations 14th

Steepest Descent Bat Algorithm for Solving Systems of Non-li...

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14th International Conference on Intelligent Computing (ICIC)

作者： Ge, Gengyu Song, Jiaxian Wang, Juan Ouyang, Aijia Zunyi Normal Univ Sch Informat Engn Zunyi 563006 Guizhou Peoples R China

ISBN: (纸本)9783319959320;9783319959337

bat algorithm (BA) is a kind of heuristic algorithm imitating the echolocation behavior of bats. In consideration of BA shortcomings such as that it could easily fall into traps like local optimum, low accuracy and premature convergence, a new algorithm is proposed by combining steepest descent (SD) algorithm and bat algorithm based on their respective advantages and disadvantages so as to achieve the goal of solving systems of non-linear equations effectively. The results of simulation experiments show that this proposed algorithm (SD-BA) can help improve the accuracy of problem solving and make the optimization results more accurate, and therefore, it is a very efficient and reliable algorithm for solving systems of non-linear equations.

关键词： bat algorithm Steepest Descent algorithm Hybrid algorithm Systems of non-linear equations Searching

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Implementation of bat algorithm as Maximum Power Point Tracking Technique for Photovoltaic System Under Partial Shading Conditions 10

Implementation of BAT Algorithm as Maximum Power Point Track...

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10th IEEE Annual Energy Conversion Congress and Exposition (ECCE)

作者： Tey, Kok Soon Mekhilef, Saad Seyedmahmoudian, Mehdi Univ Malaya Dept Comp Syst & Technol Kuala Lumpur Malaysia Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab PEARL Kuala Lumpur Malaysia Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia

ISBN: (纸本)9781479973125

Partial shading condition is one of the main problem in the implementation of PV system as electricity generation. Thus, a maximum power point tracking (MPPT) technique which able to track the global maximum power point (GMPP) is very important. This paper introduces a MPPT algorithm which is able to track the GMPP by using the bat optimization algorithm. Generally, the disadvantages of using optimization algorithm as MPPT method is the long tracking time required. The proposed bat algorithm has the capability to converge rapidly towards the GMPP along the searching process by using the variable pulse rate and loudness parameters. The simulation and experimental results of the proposed algorithm shows that the GMPP is able to be tracked within 1.5 seconds accurately for different partial shading condition.

关键词： Maximum Power Point Tracking bat algorithm Photovoltaic system and SEPIC

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A Hybrid Grey Wolf-bat algorithm for Global Optimization 3rd

A Hybrid Grey Wolf-Bat Algorithm for Global Optimization

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3rd International Conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： ElGayyar, Mohammed Emary, E. Sweilam, N. H. Abdelazeem, M. Cairo Univ Dept Math Fac Sci Giza Egypt Cairo Univ Fac Comp & Informat Giza Egypt Natl Res Inst Astron & Geophys NRIAG Helwan Egypt

ISBN: (纸本)9783319746906;9783319746890

Bio-inspired algorithms are now becoming powerful methods for solving many real-world optimization problems. In this paper, we propose a hybrid approach involving Grey Wolf optimizer (GWO) and bat swarm optimizer (BA) for global function optimization problems. GWO is well known for its balanced exploration/exploitation behavior, while BA is known to be more exploitative due to its low exploration ability in some conditions. We use GWO exploration skills to explore the search space effectively and BA local search capabilities to refine the solution. In our hybrid algorithm, namely (GWOBA), GWO is used to explore the problem space alone and pass the best two solutions to BA to guide its local search, then BA digs deeper and find the best solution. The new proposed approach has been tested using 30 standard benchmark functions from CEC2017 benchmark suite. The performance of the hybrid algorithm has been compared to the original GWO, BA and the Whale optimization algorithm (WOA). We use a set of performance indicators to evaluate the efficiency of the method. Results over various dimensions show the superiority of the proposed algorithm.

关键词： Optimization Greywolf optimizer bat algorithm CEC2017 Hybridization

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Cost Optimization in Home Energy Management System using Genetic algorithm, bat algorithm and Hybrid bat Genetic algorithm 32

Cost Optimization in Home Energy Management System using Gen...

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32nd IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Latif, Urva Javaid, Nadeem Zarin, Syed Shahab Naz, Muqaddas Jamal, Asma Mateen, Abdul COMSATS Inst Informat Technol Islamabad 44000 Pakistan

ISBN: (纸本)9781538621950

Home energy management systems are widely used to cope up with the increasing demand for energy. They help to reduce carbon pollutants generated by excessive burning of fuel and natural resources required for energy generation. They also save the budget needed for installing new power plants. Price based automatic demand response (DR) techniques incorporated in these systems shift appliances from high price hours to low price hours to reduce electricity bills and peak to average ratio (PAR). In this paper, electricity load of home is categorized into three types: base load, shift-able interruptible load and shiftable non-interruptible load. In literature many metaheuristic optimization techniques have been implemented for scheduling of appliances. In this work for the optimization of energy usage genetic algorithm (GA) and bat algorithm (BA) are implemented with time of use (TOU) pricing scheme to schedule appliances to reduce electricity bills, the peak to average ratio and appliance delay time. A new technique bat genetic algorithm (BGA) has been proposed. It is hybrid of GA and BA. It outperforms GA and BA in terms of cost reduction and peak to average ratio for single home scenario as well as multiple home scenario. Operation time internals (OTIs) 15 minutes, 30 minutes and 1 hour have been considered to check their effect on cost reduction, PAR and user comfort (UC).

关键词： Home energy management demand side management genetic algorithm bat algorithm hybrid scheme

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A Kernelized Fuzzy C-means Clustering algorithm based on bat algorithm 2018

A Kernelized Fuzzy C-means Clustering Algorithm based on Bat...

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10th International Conference on Computer and Automation Engineering (ICCAE)

作者： Cheng, Chunying Bao, Chunhua Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao Peoples R China

ISBN: (纸本)9781450364102

To overcome the defects of easily falling into local optimum and being sensitive to initial values brought by kernelized fuzzy means clustering algorithm (KFCM), a kernelized fuzzy means clustering algorithm based on bat algorithm (BA-KFCM) is proposed in this paper. In this paper, IRIS dataset, Glass dataset and Wine dataset in the classical datasets are used to simulate the experiment respectively, and the results of the algorithm are compared with those of the particle swarm optimization algorithm and the firefly algorithm so as to verify the effectiveness of the algorithm. The experimental results show that the proposed algorithm is superior to other algorithms in terms of effects and has a better quality of clustering.

关键词： bat algorithm Clustering Center Kernelized Fuzzy C-means Optimization

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bat algorithm Implementation on Economic Dispatch Optimization Problem

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Procedia Computer Science 2018年 135卷 275-282页

作者： Lili A. Wulandhari Siti Komsiyah Wisnu Wicaksono Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Power plant is one of the substantial industry in a country since it supports various needs of people. Optimum cost for running this industry is a necessity so that power generated can be produce according to power demand with appropriate cost. Economic dispatch is an optimization approach in power system plant with objective function is to minimize cost by finding appropriate arrangement of generator output according electric requirement and capacity of the system. Previous researches have been proposed techniques to solve this problem, however a stable convergence and good computational efficiency is still required. Therefore, this paper proposes bat algorithm to minimize total generator cost from thermal power plant. bat algorithm is one of nature inspired optimization problem which has advantage in stable convergence. The experiment results show that bat algorithm is able to save approximately 1.23% compare to the actual cost and 0.12% to firefly algorithm.

关键词： Economic Dispatch bat algorithm Optimization

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bat algorithm Optimizer for Drilling Trajectory Designing under Wellbore Stability Constraints

Bat Algorithm Optimizer for Drilling Trajectory Designing un...

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第37届中国控制会议

作者： Jiafeng Xu Xin Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences

In the process of deep geological resources exploration, there are often some complex stratigraphic situations. A threedimension drilling trajectory is designed by using the geological environment data. The trajectory should be sure that the target position is reachable. And it can avoid the area prone to be in an accident. In order to solve this problem, this paper discusses a three-dimensional wellbore trajectory design optimization model suitable for shale gas horizontal mining. Considering the wellbore stability and dogleg severity as constraints, the minimum drilling length is taken as the objective function. Depending on the Mohr-Coulomb criterion, the range of azimuth angle and inclination angle of safety trajectory can be determined. It can be seen as a constraint on the wellbore derrick segment. As for the optimization algorithm, bat algorithm（BA） optimizer combined with a penalty function method is applied to this optimization model. Finally, the case study shows that the BA performs well in the wellbore optimization design. It obtains the trade-off result between the safe and efficient drilling demands. And, it provides a basic model idea for the real-time trajectory optimization while drilling.

关键词： Drilling Trajectory Designing Wellbore Stability bat algorithm

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Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and bat algorithm

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WATER 2018年第6期10卷 807页

作者： Ehteram, Mohammad Othman, Faridah Binti Yaseen, Zaher Mundher Afan, Haitham Abdulmohsin Allawi, Mohammed Falah Malek, Marlinda Bt. Abdul Ahmed, Ali Najah Shahid, Shamsuddin Singh, Vijay P. El-Shafie, Ahmed Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan *** Iran Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia Ton Duc Thang Univ Fac Civil Engn Sustainable Dev Civil Engn Res Grp Ho Chi Minh City Vietnam Univ Kebangsaan Malaysia Fac Engn & Built Environm Civil & Struct Engn Dept Bangi 43600 Malaysia Univ Tenaga Nas Coll Engn Dept Civil Engn Kajang 43000 Malaysia Univ Tenaga Nas Inst Policy Energy Res IPERe Kajang 43000 Malaysia Univ Tenaga Nas Inst Energy Infrastruct Kajang 43000 Malaysia Univ Teknol Malaysia Fac Civil Engn Johor Baharu 81310 Malaysia Texas A&M Univ Dept Biol & Agr Engn 2117 TAMU College Stn TX 77843 USA Texas A&M Univ Zachry Dept Civil Engn 2117 TAMU College Stn TX 77843 USA

Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time.

关键词： bat algorithm particle swarm optimization flood routing Muskingum model

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An Improved Collaborative Filtering Based Recommender System using bat algorithm

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Procedia Computer Science 2018年 132卷 1795-1803页

作者： Sambhav Yadav Vikesh Shreyam Sushama Nagpal Netaji Subhas Institute of Technology Sector-3 Dwarka Delhi 110078 India

Recommender Systems have proven to be of great aid in dealing with the issue of Information Overload by improving the user experience through quality recommendations. In recent times, heuristic techniques have been employed by researchers in recommender systems along with traditional methods of collaborative and content based filtering. On the same account, in this work a bat algorithm based heuristic technique has been used to compute the weights of items (features) so as to find better neighbourhood for the active user. We argue and also prove using the results that this technique of giving weights to items using heuristic methods helps in achieving better personalized recommendations. The performance of this system was also compared to that of Artificial Bee Colony based system (ABC). The results indicated that BA performed 6.9% better than ABC in terms of Mean Absolute Error and F1 Score using our technique.

关键词： bat algorithm Recommender System Artificial Bee Colony algorithm Swarm Intelligence Collaborative Filtering

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