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International Conference on Radar, Communication and Computing (ICRCC)

作者： Manju, A. Nigam, M. J. Indian Inst Technol Roorkee Dept Elect & Comp Engn Roorkee Uttarakhand India

ISBN: (纸本)9781467327589;9781467327565

firefly algorithm (FA), a population based algorithm has been found superior over other algorithms in solving optimization problems. In this paper we intend to formulate a quantum Delta potential well model for FA. The fireflies are placed in an exponent atmosphere where the extinction coefficient varies with distance between the fireflies and a global updation operator with weighting function was employed. Testing the algorithm on various Benchmark functions has proven its superiority.

关键词： firefly algorithm global optimization exponential atmosphere dirac delta function

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firefly algorithm for multi-objective RFID network planning problem 22

Firefly algorithm for multi-objective RFID network planning ...

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22nd Telecommunications Forum Telfor (TELFOR)

作者： Tuba, Milan Bacanin, Nebojsa Alihodzic, Adis Megatrend Univ Fac Comp Sci Bulevar Umetnosti 29 Belgrade 11070 N Serbia Univ Sarajevo Fac Sci & Math Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9781479961917

RFID network planning involves many objectives and constraints and it belongs to the class of NP-hard problems. Such problems were recently successfully tackled by nondeterministic optimization metaheuristics where swarm intelligence represents a prominent branch. We present improved firefly algorithm adjusted for multi-objective MD network planning where our proposed algorithm improved results considering all relevant performance measures tested on the same benchmark functions and compared to the previously known results from the literature.

关键词： firefly algorithm metaheuristic optimization RFID network planning swarm intelligence

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firefly algorithm based Power Control in Wireless TV White Space Network

Firefly Algorithm based Power Control in Wireless TV White S...

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IEEE AFRICON Conference - Science, Technology and Innovation for Africa

作者： Kennedy, Ronoh George, Kamucha William, Okelo-Odongo Thomas, Olwal Tonny, Omwansa Univ Nairobi Sch Comp & Informat Nairobi Kenya Univ Nairobi Dept Elect & Informat Engn Nairobi Kenya Tshwane Univ Technol Dept Elect Engn Pretoria South Africa

ISBN: (纸本)9781538627754

TV white spaces (TVWS) can be used by secondary users as long as they do not cause harmful interference to primary users (PUs). Regulatory authorities worldwide have mandated the use of geo-location database (GLDB) for protection of incumbent users. In a wireless TVWS network, interference could be due to either co-channel interference or adjacent channel interference. Recent studies have shown that aggregate adjacent channel interference from a high density of mobile users using low power in multiple adjacent channels is as harmful as co-channel interference even if each single adjacent channel interference stays below the GLDB D/U (desired to undesired) ratio constraint. When there is a high density secondary users (SUs) in a TVWS network, there will high interference among SUs and a possibility of harmful interference to primary users (PUs). Power control is therefore necessary to protect PUs against harmful interference and to reduce the level of interference among SUs. In this paper we propose a firefly algorithm based power control algorithm for a GLDB based wireless TVWS network that take into consideration interference constraints at the primary user (PU) and SU. The algorithm also takes into consideration both co-channel and adjacent channel interference. Simulation results show that the proposed algorithm protects TV receivers against harmful interference and results in an improvement of signal to interference and noise ratio (SINR) for SUs.

关键词： Dymamic spectrum access cognitive radio geolocation database TV white spaces power control firefly algorithm

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firefly algorithm Order Batching Problem Based on Local Search Optimization 24

Firefly Algorithm Order Batching Problem Based on Local Sear...

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5th International Conference on Computing, Networks and Internet of Things (CNIOT)

作者： Miao, Yumo Jia, Luyun Yu, Han Changan Univ Changan Dublin Int Coll Transportat Xian Peoples R China

ISBN: (纸本)9798400716751

Order processing efficiency determines the stability and efficiency of the whole logistics system, which is of great significance for warehouse operation management. The order batch problem(OBP) is a combinatorial optimization problem that arises in the warehouse order picking process. In this paper, we propose to use the firefly algorithm (FA) to solve the order batch processing problem and optimize the algorithm using local search optimization. The algorithm is utilized to verify the effectiveness and efficiency of the firefly algorithm on the order batching problem for an instance. Also, a comparison highlights the usefulness of local search operations for optimizing the firefly algorithm. The experimental results show that the optimized FA exhibits faster convergence speed during the iteration process and is not easy to fall into the local optimum, and the final distance required by the picker is 1113 m. Compared with the unoptimized FA, the optimized algorithm significantly improves the optimization effect while maintaining a lower time overhead. Specifically, the average running time of the optimized FA is 10.8 seconds, which is a significant optimization effect with low additional time overhead. This study provides a new implementation idea for the solution of OBP problems and the application of FA.

关键词： Order Batch Problem firefly algorithm Local Search Optimization Combinatorial Optimization Warehouse Operations Management

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firefly algorithm with artificial neural network for time series problems

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Research Journal of Applied Sciences, Engineering and Technology 2014年第19期7卷 3978-3982页

作者： Alweshah, Mohammed Prince Abdullah Bin Ghazi Faculty of Information Technology Al-Balqa Applied University Salt Jordan

Time series classification is a supervised learning method maps the input to the output using historical data. The primary objective is to discover interesting patterns hidden in the data. For the purpose of solving time series classification problems used the multi-layered perceptrons Artificial Neural Networks (ANN). The weights in the ANN are modified to provide the output values of the net, which are much closer to the values of the preferred output. For this reason, several algorithms had been proposed to train the parameters of the neural network for time series classification problems. This study attempts to hybrid the firefly algorithm (FA) with the ANN in order to minimize the error rate of classification (coded as FA-ANN). The FA is employed to optimize the weights of the ANN model based on the processes. The proposed FA-ANN algorithm was tested on 6 benchmark UCR time series data sets. The experimental results have revealed that the proposed FA-ANN can effectively solve time series classification problems. © Maxwell Scientific Organization, 2014.

关键词： Artifitail neural networks firefly algorithm Time series problems

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firefly algorithm for Bezier Curve Approximation

Firefly Algorithm for Bezier Curve Approximation

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13th International Conference on Computational Science and Its Applications (ICCSA)

作者： Galvez, Akemi Iglesias, Andres Univ Cantabria ETSI Caminos Canales & Puertos Dept Appl Math & Computat Sci E-39005 Santander Spain

ISBN: (纸本)9780769550459

A critical problem in many applied fields is to construct the polynomial curve of a certain degree that approximates a given set of noisy data points better in the sense of least-squares. This problem arises in a number of areas, such as Computer-Aided Design & Manufacturing (CAD/CAM), virtual reality, medical imaging, computer animation, and many others. This paper introduces a new method to solve this problem through free-form Bezier curves. Our method applies a powerful metaheuristic nature-inspired algorithm, called firefly algorithm, introduced recently to solve optimization problems. The paper shows that this new approach can be effectively applied to obtain an optimal approximating Bezier curve to the set of data points with a proper selection of the control parameters. To check the performance of our approach, it has been applied to some illustrative examples of different types, including shapes with complex features such as singularities and self-intersections. Our results show that the method performs very well, being able to yield the best approximating curve with a high degree of accuracy.

关键词： Bezier curves curve approximation data fitting firefly algorithm metaheuristic

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firefly algorithm Based Optimized PID Controller for Stability Analysis of DC-DC SEPIC Converter 12

Firefly Algorithm Based Optimized PID Controller for Stabili...

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12th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

作者： Shagor, Md Rafid Kaysar Mahmud, Al Jaber Nishat, Mirza Muntasir Faisal, Fahim Mithun, Mehedi Hasan Khan, Md Ashik Islamic Univ Technol Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (纸本)9781665406901

firefly algorithm (FA) refers to a swarm intelligence-based technique which appears to be one of the most influential optimization algorithms in designing optimized controllers for power converters in recent years. Hence, an optimized PID controller is designed based on this algorithm, and stability analysis and performance enhancement of the closed-loop Single-Ended Primary Inductor Converter (SEPIC) are demonstrated comprehensively. Using the state space average technique, the SEPIC converter is mathematically modeled, and the transfer function for the closed-loop system is obtained. The performance parameters noted in the analysis of the system's stability are the percentage of overshoot (%OS), rise time (Tr), settling time (Ts), and peak amplitude (PA). However, fitness functions like integral absolute error (IAE), integral squared error (ISE), integral time squared error (ITSE), integral time absolute error (ITAE) are considered in this optimization process. Step responses based on the gain parameters are attained, and a comparative study with performance evaluation is shown between the optimized and conventional PID controller. MATLAB and Simulink are utilized for simulation.

关键词： firefly algorithm SEPIC Converter State Space Average Method PID Controller

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Energy Efficient Cluster Based Routing Protocol for WSN Using firefly algorithm and Ant Colony Optimization

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期125卷 2167-2200页

作者： Wang, Zongshan Ding, Hongwei Li, Bo Bao, Liyong Yang, Zhijun Liu, Qianlin Yunnan Univ Sch Informat Sci & Engn Kunming 650500 Yunnan Peoples R China Yunnan Educ Dept Kunming 650500 Yunnan Peoples R China Yunnan Mil Reg Kunming 650500 Yunnan Peoples R China

Maximizing network lifetime is the main goal of designing a wireless sensor network. Clustering and routing can effectively balance network energy consumption and prolong network lifetime. This paper presents a novel cluster-based routing protocol called EECRAIFA. In order to select the optimal cluster heads, Self-Organizing Map neural network is used to perform preliminary clustering on the network nodes, and then the relative reasonable level of the cluster, the cluster head energy, the average distance within the cluster and other factors are introduced into the firefly algorithm (FA) to optimize the network clustering. In addition, the concept of decision domain is introduced into the FA to further disperse cluster heads and form reasonable clusters. In the inter-cluster routing stage, the inter-cluster routing is established by an improved ant colony optimization (ACO). Considering factors such as the angle, distance and energy of the node, the heuristic function is improved to make the selection of the next hop more targeted. In addition, the coefficient of variation in statistics is introduced into the process of updating pheromones, and the path is optimized by combining energy and distance. In order to further improve the network throughput, a polling control mechanism based on busy/idle nodes is introduced during the intra-cluster communication phase. The simulation experiment results prove that under different application scenarios, EECRAIFA can effectively balance the network energy consumption, extend the network lifetime, and improve network throughput.

关键词： WSN Self-organizing map firefly algorithm Ant colony optimization Energy consumption Network lifetime Network throughput

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Optimization of wavelet neural networks with the firefly algorithm for approximation problems

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NEURAL COMPUTING & APPLICATIONS 2017年第7期28卷 1715-1728页

作者： Zainuddin, Zarita Ong, Pauline Univ Sains Malaysia Sch Math Sci Minden 11800 Penang Malaysia Univ Tun Hussein Onn Malaysia UTHM Fac Mech & Mfg Engn Batu Pahat 86400 Johor Malaysia

The effectiveness of swarm intelligence has been proven to be at the heart of various optimization problems. In this study, a recently developed nature-inspired algorithm, specifically the firefly algorithm (FA), is integrated in the learning strategy of wavelet neural networks (WNNs). The FA, which systematically optimizes the initial location of the translation parameters for WNNs, has reduced the number of hidden nodes while simultaneously improved the generalization capability of WNNs significantly. The applicability of the proposed model was demonstrated through empirical simulations for function approximation study, with both synthetic and real-world data. Performance assessment demonstrated its enhancement over the K-means clustering and random initialization approaches, as well as to the other neural network models reported in the literature, whereby a noteworthy decrease in the approximation error was observed.

关键词： firefly algorithm Function approximation Metaheuristic algorithm Swarm intelligence Wavelet neural networks

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Modified firefly algorithm for workflow scheduling in cloud-edge environment

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NEURAL COMPUTING & APPLICATIONS 2022年第11期34卷 9043-9068页

作者： Bacanin, Nebojsa Zivkovic, Miodrag Bezdan, Timea Venkatachalam, K. Abouhawwash, Mohamed Singidunum Univ Danijelova 32 Belgrade 11000 Serbia Univ Hradec Kralove Fac Sci Dept Appl Cybernet Hradec Kralove 50003 Czech Republic Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt Michigan State Univ Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA

Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users-to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives-cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results' quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.

关键词： Edge computing Swarm intelligence Workflow scheduling firefly algorithm Genetic operator Quasi-reflection-based learning

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