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Out-of-Band Radiation Reduction in Cognitive Radio OFDM Systems Hybridizing firefly algorithm with Generalized Sidelobe Canceller

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WIRELESS PERSONAL COMMUNICATIONS 2018年第3期100卷 941-956页

作者： Elahi, Atif Qureshi, Ijaz Mansoor Zaman, Fawad Gul, Noor Atif, Mehreen Int Islamic Univ Fac Engn & Technol Dept Elect Engn Islamabad 44000 Pakistan Air Univ Dept Elect Engn Islamabad 44000 Pakistan COMSATS Inst Informat Technol Dept Elect Engn Attock Campus Attock 43600 Pakistan Islamia Coll Univ Dept Stat Peshawar 49000 Pakistan

Orthogonal frequency division multiplexing is the most popular method for dynamic spectrum access because of its ability in changing the spectrum shapes. However, it produces a substantial out-of-band (OOB) radiation due to the high sidelobes of the modulated subcarriers which results in considerable interference to the neighboring users. This challenge becomes even more critical in cognitive radio networks, which gives the secondary system the right to use the available spectrum holes. In this paper, a joint technique for the reduction of OOB radiation is developed by adding the advantages of the cancellation carriers, based on firefly algorithm and of the generalized sidelobe canceller. Simulation results show that by using the proposed technique substantial reduction in OOB radiation is achieved.

关键词： Cognitive radio Cancellation carriers firefly algorithm Generalized sidelobe canceller Orthogonal frequency division multiplexing Out-of-band radiation

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Smoothing target encoding and class center-based firefly algorithm for handling missing values in categorical variable

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JOURNAL OF BIG DATA 2023年第1期10卷 10页

作者： Nugroho, Heru Utama, Nugraha Priya Surendro, Kridanto Inst Teknol Bandung Sch Elect Engn & Informat Jl Ganesha 10 Bandung 40132 Jawa Barat Indonesia

One of the most common causes of incompleteness is missing data, which occurs when no data value for the variables in observation is stored. An adaptive approach model outperforming other numerical methods in the classification problem was developed using the class center-based firefly algorithm by incorporating attribute correlations into the imputation process (C3FA). However, this model has not been tested on categorical data, which is essential in the preprocessing stage. Encoding is used to convert text or Boolean values in categorical data into numeric parameters, and the target encoding method is often utilized. This method uses target variable information to encode categorical data and it carries the risk of overfitting and inaccuracy within the infrequent categories. This study aims to use the smoothing target encoding (STE) method to perform the imputation process by combining C3FA and standard deviation (STD) and compare by several imputation methods. The results on the tic tac toe dataset showed that the proposed method (C3FA-STD) produced AUC, CA, F1-Score, precision, and recall values of 0.939, 0.882, 0.881, 0.881, and 0.882, respectively, based on the evaluation using the kNN classifier.

关键词： Missing data Encoding Smoothing firefly algorithm Class center

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An improved firefly algorithm for collaborative manufacturing chain optimization problem

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2019年第6期233卷 1711-1722页

作者： Zhang, Fuqiang Hui, Jizhuang Zhu, Bin Guo, Yunxin Changan Univ MOE Key Lab Rd Construct Technol & Equipment Xian 710064 Shaanxi Peoples R China

In order to conduct resource sharing and deployment in cloud manufacturing environment, a concept of collaborative manufacturing chain was proposed. Based on machining tasks with the sequential characteristics, the proposed model considering the criteria of service cost, service time, service quality and service utilization was constructed. Fuzzy analytical hierarchy process was adopted to add the above multi-criteria model to a single objective problem. Then, an improved firefly algorithm was used to solve a reasonable collaborative manufacturing chain scheme. Based on the discrete characteristics of the collaborative manufacturing chain, iterative position function was improved to make the solution space to be a discrete domain. Furthermore, particle swarm optimization was used to optimize the step length factor alpha, attraction degree beta(0) and light absorption coefficient gamma so as to prevent the firefly algorithm from local optimum. Compared with the genetic algorithm, numerical result suggests that the improved firefly algorithm has more advantages in convergence speed and solving efficiency. It is expected that this study can provide a useful reference for the service composition of collaborative manufacturing chain.

关键词： Cloud manufacturing collaborative manufacturing chain firefly algorithm particle swarm optimization fuzzy analytical hierarchy process

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An Improved Fuzzy Adaptive firefly algorithm-Based Hybrid Clustering algorithms

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INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS 2021年第SUPPL 2期29卷 259-278页

作者： Agrawal, Anmol Tripathy, B. K. Thirunavukarasu, Ramkumar Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India

The implication of firefly and fuzzy firefly optimization algorithms has been greatly witnessed in clustering techniques and extensively used in applications such as Image segmentation. Parameters such as step factor and attractiveness have been kept constant in these algorithms, which affect the convergence rate and accuracy of the clustering process. Though fuzzy adaptive firefly algorithm tackled this problem by making those parameters an adaptive one, issues such as low convergence rate, and provision of non-optimal solutions are still there. To tackle these issues, this paper proposed a novel fuzzy adaptive fuzzy firefly algorithm that significantly improves the accuracy and convergence rate while comparing with the existing optimization algorithms. Further, the proposed algorithm fused with existing hybrid clustering algorithms involving fuzzy set, intuitionistic fuzzy set, and rough set resulted in eight novel hybrid clustering algorithms which lead to better performance in optimizing the selection of initial centroids. To validate the proposal, experimental studies have been conducted on datasets found in bench-marking data repositories such as UCI, and Kaggle. The performance and accuracy evaluation of proposed algorithms have been carried out with the aid of seven accuracy measures. Results clearly indicate the improved accuracy and convergence rate of the proposed algorithms.

关键词： Data clustering firefly algorithm fuzzy adaptive firefly fuzzy adaptive fuzzy firefly optimization algorithms soft clustering

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A new approach to optic disc detection in human retinal images using the firefly algorithm

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2016年第2-3期54卷 453-461页

作者： Rahebi, Javad Hardalac, Firat Gazi Univ Dept Elect & Elect Engn Ankara Turkey

There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.

关键词： firefly algorithm Optic disc detection Retinal images

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Optimal Location of Thyristor-controlled Series Capacitor to Enhance Power Transfer Capability Using firefly algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2014年第14期42卷 1541-1552页

作者： Bathina, Venkateswara Rao Gundavarapu, Venkata Nagesh Kumar Gandhi Inst Technol & Management GITAM Univ Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India

Abstract-The optimal power flow problem seeks to find an optimal profile of active and reactive power generations along with voltage magnitudes in such a manner as to minimize the total operating costs of a power system while satisfying network security constraints. This article presents a firefly algorithm to solve the optimal power flow problem incorporating a thyristor-controlled series capacitor. A thyristor-controlled series capacitor is considered to find the optimal location in transmission lines to enhance the power transfer capability of the transmission line. To assess the effectiveness of the proposed algorithm, it was tested on a 5-bus test system, an IEEE 14-bus system, and a modified IEEE 30-bus system, and it was compared with the genetic algorithm and differential evolution with and without a thyristor-controlled series capacitor. It has also been observed that the proposed algorithm can be applied to larger systems and does not suffer with computational difficulties. The results show that the firefly algorithm produces better results than others and has fast computing time for solving the optimal power flow problem with a thyristor-controlled series capacitor.

关键词： genetic algorithm optimization differential evolution firefly algorithm generation reallocation thyristor-controlled series capacitor flexible AC transmission system device optimal location power system stability optimal power flow

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An optimal design for type-2 fuzzy logic system using hybrid of chaos firefly algorithm and genetic algorithm and its application to sea level prediction

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第3期27卷 1335-1346页

作者： Long, Nguyen Cong Meesad, Phayung King Mongkuts Univ Technol North Bangkok Fac Informat Technol Bangkok Thailand

This paper proposes an optimal design for interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic system. In this method, the fuzzy c-means clustering algorithm is used to determine structure of fuzzy rule as well as number of rules. A hybrid between chaos firefly algorithm and genetic algorithms (CFGA) is developed, which is used to find the desirable parameters of membership functions and consequents parameters of the fuzzy logic system. The obtained optimal fuzzy logic system is used to predict sea water level in short-term and long-term horizontal. To demonstrate the superiority of the hybrid algorithm in design the fuzzy logic system, comparison between CFGA with genetic algorithms and firefly algorithm applied to optimize the fuzzy logic system for sea water level prediction is investigated. Results illustrate CFGA approach to design fuzzy logic system to be highly comparative, outperforming both genetic algorithms and firefly algorithm.

关键词： Genetic algorithms firefly algorithm interval type-2 fuzzy logic system sea water level prediction

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Continuous versions of firefly algorithm: a review

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ARTIFICIAL INTELLIGENCE REVIEW 2019年第3期51卷 445-492页

作者： Tilahun, Surafel Luleseged Ngnotchouye, Jean Medard T. Hamadneh, Nawaf N. Univ Zululand Dept Math Sci Private Bag X1001 ZA-3886 Kwa Dlangezwa South Africa Univ KwaZulu Natal Sch Math Stat & Comp Sci Private Bag X01 ZA-3209 Pietermaritzburg South Africa Saudi Elect Univ Dept Basic Sci Coll Sci & Theoret Studies Riyadh 11673 Saudi Arabia

firefly algorithm is a swarm based metaheuristic algorithm designed for continuous optimization problems. It works by following better solutions and also with a random search mechanism. It has been successfully used in different problems arising in different disciplines and also modified for discrete problems. Unlike its easiness to understand and to implement;its effectiveness is highly affected by the parameter values. In addition modifying the search mechanism may give better performance. Hence different modified versions are introduced to overcome its limitations and increase its performance. In this paper, the modifications done on firefly algorithm for continuous optimization problems will be reviewed with a critical analysis. A detailed discussion on the modifications with possible future works will also be presented. In addition a comparative study will be conducted using forty benchmark problems with different dimensions based on ten base functions. The result shows that some of the modified versions produce superior results with a tradeoff of high computational time. Hence, this result will help practitioners to decide which modified version to apply based on the computational resource available and the sensitivity of the problem.

关键词： firefly algorithm Optimization Bio-inspired algorithm Swarm intelligence

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A firefly algorithm for power management in wireless sensor networks (WSNs)

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JOURNAL OF SUPERCOMPUTING 2021年第9期77卷 9411-9432页

作者： Pakdel, Hossein Fotohi, Reza Islamic Azad Univ Cent Tehran Branch Dept Comp Engn Tehran Iran Shahid Beheshti Univ Fac Comp Sci & Engn Tehran *** Iran

In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize the network topology. Therefore, choosing the right route in this type of network due to resource constraints and their operating environment is one of the most important challenges in these networks. Therefore, the main purpose of these networks is to collect appropriate routing information about the environment around the network sensors while observing the energy consumption of the sensors. One of the important approaches to reduce energy consumption in sensor networks is the use of the clustering technique, but in most clustering methods, only the criterion of the amount of energy of the cluster or the distance of members to the cluster has been considered. Therefore, in this paper, a method is presented using the firefly algorithm and using the four criteria of residual energy, noise rate, number of hops, and distance. The proposed method called EM-firefly is introduced which selects the best cluster head with high attractiveness and based on the fitness function and transfers the data packets through these cluster head to the sink. The proposed method is evaluated with NS-2 simulator and compared with the algorithm-PSO and optimal clustering methods. The evaluation results show the efficiency of the EM-firefly method in maximum relative load and network lifetime criteria compared to other methods discussed in this article.

关键词： Wireless sensor networks (WSNs) Power management Network lifetime firefly algorithm

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Optimization of feedback bits using firefly algorithm for interference reduction in LTE femtocell networks

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SOFT COMPUTING 2020年第20期24卷 15361-15371页

作者： Hariharan, S. Chikte, Kartiki Shankar, T. Rajesh, A. Sulthana, S. Vellore Inst Technol Sch Elect Engn Vellore 632014 Tamil Nadu India SRM Inst Sci & Technol Fac Engn & Technol Dept Mechatron Engn Kattankulathur 603203 Tamil Nadu India

Femtocells are the feasible solutions to extend the network coverage of indoor users and to enhance the network capacity in long-term evolution advanced (LTE-A)-based 5G networks. However, the femtocell base station shares the same frequency spectrum of microcell base station in unplanned manner. Hence, interference mitigation is a crucial problem in densely deployed femtocell environment and it is more severe with the deployment of femtocells in LTE-A network. In this paper, a modified dirty paper coding is proposed for interference mitigation along with the optimization of feedback bits using natural inspired meta-heuristic firefly algorithm. The proposed meta-heuristic algorithm reduces the interference by periodically unicasting the channel state information. Since the bandwidth of feedback system is limited, it is optimized in such a way that it does not affect the performance of the system. As compared to the conventional zero-forcing pre-coding, the proposed modified dirty paper coding along with firefly algorithm scheme offers improved sum rate of 70% and 64% with increase in the number of feedback bits and number of users, respectively.

关键词： Femtocell firefly algorithm Modified dirty paper coding

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