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Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第9期36卷 2461-2476页

作者： Shaibani, Mohammad Javad Emamgholipour, Sara Moazeni, Samira Sadate Univ Tehran Med Sci Sch Publ Hlth Dept Hlth Management & Econ Tehran Iran Zahedan Univ Med Sci Sch Nursing & Midwifery Med Surg Nursing Dept Zahedan Iran

As an ongoing public health menace, the novel coronavirus pandemic has challenged the world. With several mutations and a high transmission rate, the virus is able to infect individuals in an exponential manner. At the same time, Iran is confronted with multiple wave peaks and the health care system is facing a major challenge. In consequence, developing a robust forecasting methodology can assist health authorities for effective planning. In that regard, with the help of Artificial Neural Network-Artificial Bee Colony (ANN-ABC) and Artificial Neural Network- firefly algorithm (ANN-FA) as two robust hybrid artificial intelligence-based models, the current study intends to select the optimal model with the maximum accuracy rate. To do so, first a sample of COVID-19 confirmed cases in Iran ranging from 19 February 2020 to 25 July 2021 is compiled. 75% (25%) of total observation is randomly allocated as training (testing) data. Afterwards, an ANN model is trained with Levenberg-Marquardt algorithm. Accordingly, based on R-squared and root-mean-square error criteria, the optimal number of hidden neurons is computed as 17. The proposed ANN model is employed to develop ANN-ABC and ANN-FA models for achieving the maximum accuracy rate. According to ANN-ABC, the R- squared values of the optimal model are 0.9884 and 0.9885 at train and test stages. In respect to ANN-FA, the R-squared ranged from 0.9954 to 0.9940 at the train and test phases, which indicates the outperformance of ANN-FA for predicting COVID-19 new cases in Iran. Finally, the proposed ANN-ABC and ANN-FA are applied for simulating the COVID-19 new cases data in different countries. The results revealed that both models can be used as a robust predictor of COVID-19 data and in a majority of cases ANN-FA outperforms the ANN-ABC.

关键词： COVID-19 Artificial neural network Artificial bee colony firefly algorithm Hybrid model

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firefly algorithm with non-homogeneous population: A case study in economic load dispatch problem

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2021年第3期72卷 519-534页

作者： Kapelinski, Kleber Juchem Neto, Joao Plinio dos Santos, Eduardo Machado Univ Fed Pampa UNIPAMPA Alegrete Brazil Univ Fed Rio Grande do Sul Porto Alegre RS Brazil

The economic load dispatch problem is a very important practical problem in electric power systems operation, where an efficient solution can lead to cost and emissions reductions. The use of metaheuristics, as the firefly algorithm, for the solution of economic load dispatch problems has been shown to be a more appropriate alternative than classical optimisation methods. In this work, we have proposed a new variant for the firefly algorithm, considering a non-homogeneous population. This new variant was validated using ten benchmark functions, and then applied to solve a 15-units economic load dispatch problem, considering transmission line losses, power limits, ramp limits and prohibited zones for each generator, and a 13-units non-convex system with valve-point loading effect in the cost function and power generation limits. The results show that the proposed non-homogeneous variant is able to reach better solutions than the original firefly algorithm, using both penalty and non-penalty power balance constraint handling methods. In general, the non-penalty approach reached better results than the penalty method. Moreover, the new variant presented relatively competitive results for the economic load dispatch problems considered, when compared to the best results found in the literature.

关键词： firefly algorithm non-homogeneous population metaheuristics optimisation economic load dispatch problem

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Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction

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SOFT COMPUTING 2023年第6期27卷 3245-3282页

作者： Melin, Patricia Sanchez, Daniela Monica, Julio Cesar Castillo, Oscar Tijuana Inst Technol Tijuana Mexico

In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.

关键词： Ensemble neural networks COVID-19 Time series prediction Type-2 fuzzy logic firefly algorithm

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A novel cluster head selection using Hybrid Artificial Bee Colony and firefly algorithm for network lifetime and stability in WSNs

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CONNECTION SCIENCE 2022年第1期34卷 387-408页

作者： Sengathir, J. Rajesh, A. Dhiman, Gaurav Vimal, S. Yogaraja, C. A. Viriyasitavat, Wattana CVR Coll Engn Dept IT Rangareddi Telangana India CVR Coll Engn Dept ECE Rangareddi Telangana India Govt Bikram Coll Commerce Dept Comp Sci Patiala Punjab India Ramco Inst Technol Dept Artificial Intelligence & Data Sci Rajapalayam India Chulalongkorn Univ Dept Stat Business Informat Technol Div Fac Commerce & Accountancy Bangkok Thailand

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and firefly algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches.

关键词： Network lifetime energy stability Modified Artificial Bee Colony algorithm firefly algorithm cluster head selection

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Application-specific clustering in wireless sensor networks using combined fuzzy firefly algorithm and random forest

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 210卷

作者： Esmaeili, Hojjatollah Hakami, Vesal Bidgoli, Behrouz Minaei Shokouhifar, Mohammad Iran Univ Sci & Technol Sch Comp Engn Tehran Iran Iran Univ Sci & Technol Ctr Excellence Future Networks Sch Comp Engn Tehran Iran Shahid Beheshti Univ Dept Elect & Comp Engn Tehran Iran

Clustering in wireless sensor networks (WSNs) has proved to be one of the most efficient ways to hierarchically organize the network topology for the purposes of load-balancing and elongating the network lifetime. However, achieving optimal clustering in WSNs is an NP-hard problem, and consequently, heuristics and metaheuristics have been widely adopted. In this paper, a combined clustering technique based on a fuzzy-firefly algorithm (FFA) and random forest (RF), shortly named as: FFA-RF, is presented as an application-specific routing protocol for WSNs. Our FFA-RF protocol entails offline tuning and online routing phases: the offline phase consists of data collection using FFA, training and test of the RF, while the online phase is the actual application of the FFA-RF model to new network instances. In the offline phase, we construct an optimized fuzzy inference system based on FFA and apply it on different network topologies, to collect a comprehensive dataset. We then divide the resulting dataset into training and test sets to train and test the RF model. In the online phase, the trained RF model is used as an online clustering algorithm to estimate the fuzzy priority factor of the nodes for being cluster heads (CHs) in new network instances. To increase the generalizability of the RF for different configurations, node features as well as application features are used as inputs of the RF model. Simulation results for different network topologies demonstrate the superiority of the proposed FFA-RF protocol in prolonging the application -specific lifetime when compared against existing crisp heuristic, fuzzy heuristic, metaheuristic, and combined fuzzy-metaheuristic protocols.

关键词： Wireless sensor networks Application -specific clustering Machine learning Fuzzy inference system firefly algorithm Random forest

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A novel opposition based improved firefly algorithm for multilevel image segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第11期81卷 15521-15544页

作者： Sharma, Abhay Chaturvedi, Rekha Bhargava, Anuja MIT ADT Univ MIT SOE Pune Maharashtra India GLA Univ Mathura India

The data explosion caused by the Internet and its applications has given researchers immense scope for data analysis. A large amount of data is available in form of images. Image processing is required for better understandability of an image. Various image processing steps are available for improving the image in different application areas. Various applications like medical imaging, face recognition, biometric security, and traffic surveillance, etc. depend only on image and its analysis. This analysis in several applications is highly dependent on the outcome of image segmentation. This paper focuses on good segmentation through multi-level thresholding. In this research, the algorithm includes two modules related to Entropy and variance. The first module is concerned with the modified firefly algorithm (FA) with Kapur's, Tsallis, and Fuzzy Entropy. FA is used to optimize fuzzy parameters for obtaining optimal thresholds. The second module is derived from the principle of variance between two classes known as between variance or inter-cluster variance. The opposition-based the learning method is used for initializing the population of candidate solutions and levying flight and local search is implemented with FA. The various experiments have been performed on Berkeley and benchmark images with distinct threshold (i.e. 2, 3, 4, 5) values. The proposed algorithm has been estimated and compared with known metaheuristic optimization methods like particle swarm optimization (PSO) and electromagnetism optimization (EMO). The results have been assessed quantitatively and qualitatively by using parameters like Peak signal-to-noise ratio (PSNR), structured similarity index metric (SSIM), objective function values, and convergence curve. The algorithm proposed observed better experiment results than PSO, EMO in terms of persistency and quality.

关键词： Entropy Variance Segmentation Thresholding firefly algorithm Optimization

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Polygonal approximation for digital image boundary using firefly algorithm

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JOURNAL OF ELECTRONIC IMAGING 2022年第6期31卷 062003-062003页

作者： Raja, Kiruba Thangam Ray, Bimal Kumar Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India

Polygonal approximation (PA) is a challenging problem and has its wide applications in computer vision, electronic imaging, image classification, image visualization, pattern recognition and image analysis. This paper proposes a stochastic technique-based firefly algorithm (FA) for PA. FA customizes a kind of randomization by searching a set of solutions where PA requires more combinations of approximation to find an optimal solution. The significant feature of the FA is real random sample generation and attractive features. The attractiveness and brightness of the firefly have been used efficiently to solve the approximation problem by detecting the dominant points (DPs) with fewer iterations to produce better results. Thus, this technique achieves the main goal of PA that is a minimum error value with a lesser number of DPs. While compared with other similar algorithms, FA is independent of velocities which are considered as an advantage for this algorithm. Subsequently, the multiswarm nature of FA allows finding multiple optimal solutions concurrently. The experimental results show that the proposed algorithm generates better solutions compared with other algorithms. (c) 2022 SPIE and IS&T

关键词： polygonal approximation firefly algorithm error value dominant point multiswarm nature velocities optimal solutions integral square error

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A Multi-objective Optimum Distributed Generation Placement Using firefly algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第2期17卷 945-953页

作者： Anbuchandran, S. Rengaraj, R. Bhuvanesh, A. Karuppasamypandiyan, M. JCT Coll Engn & Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India SSN Coll Engn Dept Elect & Elect Engn Chennai Tamil Nadu India PSN Coll Engn & Technol Dept Elect & Elect Engn Tirunelveli Tamil Nadu India Kalasalingam Acad Res & Educ Dept Elect & Elect Engn Krishnankoil India

Nowadays, the power systems face several environmental and economic challenges and Distributed Generations (DGs) will be an effectual solution for them. The integration of DGs may result in power system volatility and losses. The optimal allocation of DGs will resolve the aforesaid issues. This study aims to implement multi-objective firefly algorithm for siting and sizing of DGs by optimizing six dissimilar objective functions such as minimization of power losses, improvement of voltage profile, enhancement of Voltage Stability Index, reduction of pollutant emission and elimination of average voltage Total Harmonic Distortion. Besides, fuzzy decision-making methodology has been deployed to choose one of the Pareto-optimal solutions as the Best Compromise Solution. The studies have been conducted on standard IEEE 33-bus system and a practical 62 bus Indian Utility System namely Tamil Nadu Generation and Distribution Corporation Limited as a real-world distribution network. The outcomes of the proposed work have been compared with related past studies and prominent improvement has been experienced.

关键词： Distributed generations Optimal placement firefly algorithm Power losses minimization Voltage profile improvement Pollutant minimization Total harmonic distortion minimization IEEE33-bus system TANGEDCO

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IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第2期19卷 1280-1303页

作者： Wang, Jiushuang Liu, Ying Feng, Huifen Beijing Jiaotong Univ Natl Engn Lab Interconnect Technol Next Generat I Beijing Peoples R China

Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.

关键词： software-defined internet of things distributed denial of service firefly algorithm convolutional neural network detect attacks

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Design of type-2 fuzzy logic controller optimized with firefly algorithm for maximum power point tracking of photovoltaic system based on super lift Luo converter

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2022年第4期35卷

作者： Kececioglu, O. Fatih Kahramanmaras Sutcu Imam Univ Fac Engn Dept Elect & Elect Kahramanmaras Turkey

The efficiency is a very important parameter for photovoltaic systems. Positive output elementary super lift Luo converter classified as high gain DC-DC converter are used in photovoltaic systems because of their advantages such as high efficiency, reduced ripples, and low switching loss. The drawback of the photovoltaic system based on this converter is its sensitivity to small changes on the control signal. Both maximum power point tracking and control signal stability require a powerful control system as this is a challenging task for the photovoltaic system. Interval type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainties better than type-1 fuzzy logic systems. In this work, interval type-2 Takagi-Sugeno-Kang fuzzy logic controller was first designed as a controller. Then, the controller, which was proposed for this paper, was optimized with the firefly algorithm to improve the control performance. Performance of controllers is evaluated via comparative simulation studies under the most demanding environmental conditions. It has been observed that the proposed controller reduces the settling time by more than 50% and increases the efficiency up to 10% according to the simulation situations.

关键词： firefly algorithm MPPT super lift Luo converter Type-2 FLC

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