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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 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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Design and development of symmetrical super-lift DC-AC converter using firefly algorithm for solar-photovoltaic applications

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IET CIRCUITS DEVICES & SYSTEMS 2020年第3期14卷 261-269页

作者： Albert, Johny Renoald Stonier, Albert Alexander Vivekanandha Coll Engn Women Dept Elect & Elect Engn Namakkal 637205 Tamil Nadu India Kongu Engn Coll Dept Elect & Elect Engn Erode 638060 Tamil Nadu India

The super-lift technique is an exceptional contribution to DC-DC conversion technology. A replacement approach of symmetrical super-lift multilevel inverter (SLMLI) DC/AC technology is proposed with a reduced number of elements compared with the traditional multilevel inverter. In this method, the firefly algorithm conveys a leading task for the SLMLI topology for solar-photovoltaic applications. It generates low distortion output and consumes the harmonic band of the fast Fourier transform framework by the employment of the proposed algorithm. The simulation circuit for 15 levels output uses single switch super-lift inverter feed with different kinds of load (R, RL and RLE) conditions. The power quality is improved in SLMLI with minimised harmonics underneath the various modulation indices while varied from 0.1 up to 0.8. The circuit is designed in a field-programmable gate array, which includes the firefly rule to help the multilevel output, to reduce the lower order harmonics and to find the best switching angle. As a result, the minimum total harmonic distortion from the simulation and hardware circuit is achieved. Due to the absence of bulky switches, inductor and filter elements expose the effectiveness of the proposed system.

关键词： DC-DC power convertors harmonic distortion PWM invertors fast Fourier transforms field programmable gate arrays invertors DC-AC power convertors solar-photovoltaic applications low distortion output fast Fourier transform framework single switch super-lift inverter feed firefly rule multilevel output minimum total harmonic distortion super-lift DC-AC converter firefly algorithm super-lift technique DC-DC conversion technology traditional multilevel inverter SLMLI topology

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Object-based feature extraction for hyperspectral data using firefly algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第6期11卷 1277-1291页

作者： Shahdoosti, Hamid Reza Tabatabaei, Zahra Hamedan Univ Technol Dept Elect Engn Hamadan 65155 Hamadan Iran

Object-based classification methods can improve the accuracy of hyperspectral image classification due to the fact that they incorporate spatial information into the classification procedure by assigning neighboring pixels into the same class. In this paper, a new object-based feature extraction method is proposed which makes use of information theory to reduce the Bayes error. In this way, the proposed method exploits higher order statistics for feature extraction which are very effective for non Gaussian data such as hyperspectral images. The criterion to be minimized is composed of three mutual information terms. The first and second terms, consider the maximal relevance and minimal redundancy, respectively, while the third term takes into account the segmentation map containing disjoint spatial regions. To obtain the segmentation map, we apply the firefly clustering algorithm whose fitness function simultaneously considers the intra-distance between samples and their cluster centroids, and inter-distance between centroids of any two clusters. Our experimental results, performed using a variety of hyperspectral scenes, indicate that the proposed framework gives better classification results than some state-of the-art spectral-spatial feature extraction methods.

关键词： Feature extraction firefly algorithm Mutual information Higher order statistics Object-based classification

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A dynamic adaptive firefly algorithm with globally orientation

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MATHEMATICS AND COMPUTERS IN SIMULATION 2020年 174卷 76-101页

作者： Liu, Jingsen Mao, Yinan Liu, Xiaozhen Li, Yu Henan Univ Inst Intelligent Networks Syst Kaifeng Peoples R China Henan Univ Coll Software Kaifeng Peoples R China Henan Univ Inst Management Sci & Engn Kaifeng Peoples R China

This paper proposes a dynamic adaptive firefly algorithm to overcome the disadvantages of the standard firefly algorithm, to improve the convergence rate and solution precision, and to avoid the premature algorithm trapping at the local extreme. It has a global-oriented moving mechanism and can dynamically adjust the step size and attractiveness. First, through the adaptive deviation degree strategy of optimal distance combining with the Gaussian distribution, it optimizes the fixed step-factor to balance the exploration and excavation capabilities of the algorithm and improves the diversity of the population. Second, minimum attractiveness is introduced to the algorithm, and is adaptively changed with the number of iterations, which can avoid random walk due to lack of traction between fireflies. Finally, this paper improves the mobility mechanism based on the position of the current optimal firefly. It enables firefly move with global orientation and also expands the sharing of information between fireflies to improve the overall evolutionary optimization performance of the algorithm. Theoretical analysis proves the convergence and time complexity of the improved algorithm. The simulation results of several test functions and engineering constraint optimization problems show that the improved algorithm has better solution performance, and clearly improves the convergence speed and solution accuracy. (C) 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： firefly algorithm Adaptive step size Gaussian distribution Speed reducer Three-bar truss

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