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A Novel Oppositional Chaotic flower pollination optimization algorithm for Automatic Tuning of Hadoop Configuration Parameters

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BIG DATA 2020年第3期8卷 218-234页

作者： Sathyanarayanan, Vidhyasagar Bellamkonda Jeevarathinam, Raja Paul Perinbam Marudhamuthu, Krishnamurthy SRM Inst Sci & Technol Dept Comp Sci & Engn Vadapalani Campus Chennai Tamil Nadu India Kings Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India KCG Coll Technol Dept Comp Sci & Engn Chennai Tamil Nadu India

At present, due to the introduction of the big data era, numerous numbers of data are generated consistently. Many applications utilize big data platforms, namely Spark, Hadoop, Amazon web services, and so on, since these platforms use several parameters for tuning that further enhance the operating performances. It requires a long duration of time to tune the parameters because of the complex relationship and large quantity of parameters. As a result, the building of such parameters and performance optimization at a particular duration of time becomes a challenging task. Several auto-tuning approaches are developed to achieve an optimal design. However, these approaches increase the computation time and minimize the efficiency of the cluster. It is necessary to tune the parameters automatically with low computational and processing time as well as to improve the performance of the system. In this proposed approach, a novel automatic parameter tuning system named as Opt. Tuner is proposed to select the Hadoop configuration parameters with less computational time. The optimization of the proposed approach is achieved by the flower pollination algorithm. Here, a chaotic mapping along with Opposition-Based Learning is introduced for population initialization to form a novel Oppositional Chaotic flower pollination algorithm. The main motive of this initialization phase involves in generating better individuals and to guide the search agent more quickly. In this novel approach, 15 configuration parameters are considered for auto-tuning. Finally, the performance of the proposed approach utilizes the wordcount and sort application to investigate the exhibition and proficiency of diverse databases.

关键词： flower pollination optimization algorithm Hadoop configuration parameters big data MapReduce chaotic mapping

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Hybrid Mutualism Mechanism-Inspired Butterfly and flower pollination optimization algorithm for Lifetime Improving Energy-Efficient Cluster Head Selection in WSNs

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期128卷 1567-1601页

作者： Pratha, S. Jaya Asanambigai, V Mugunthan, S. R. Annamalai Univ Dept CSE Chidambaram 608002 Tamil Nadu India Dr NGP Inst Technol Dept CSE Coimbatore 641048 Tamil Nadu India

Rapid advancement of technologies in Wireless Sensor Networks is attracting maximized attention across the scientific community due to its suitability and diversified coverage in real life applications. WSNs due to their features of resource limitation and infrastructure-less deployment introduces the most challenging issues of network lifetime improvement, energy stability and reliable cluster head selection, which is still a herculean task. Clustering is an indispensable mechanism employed for selecting an optimal cluster head with the objective of extending network lifetime with energy stability that achieves efficient data transmission. Cluster head selection through meta-heuristic algorithms introduces the merits of simplicity, flexibility, derivation free and prevents local optima. In this paper, Hybrid Mutualism Mechanism-inspired Butterfly and flower pollination optimization algorithm (HMMB-FPOA) is proposed for energy-efficient cluster head selection that attributes towards better energy stability and sustained network lifetime. Mutualism phase includes the symbiosis organisms search over flower pollination optimization algorithm for incorporating strong exploitation capability into butterfly optimization algorithm that prevents losses of premature convergence as it may lose its diversity. This integration of FPOA and BOA improves the overall exploration ability to the expected level, such that convergence speed of the algorithms is accelerated. It also balances the capabilities of exploitation and exploration by dynamically increasing the adaptive switching probability that results in prolonged network lifetime. The simulation results of HMMB-FPOA confirmed an enhanced performance of the network in terms of alive nodes, dead nodes, residual energy, overall throughput, and convergence rate on par with the existing competitive cluster head selection algorithms.

关键词： Mutualism mechanism Cluster head selection Butterfly optimization algorithm flower pollination optimization algorithm Symbiosis organisms search

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Design of solar and energy storage systems fed reduced switch multilevel converter with flower pollination optimization

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JOURNAL OF ENERGY STORAGE 2024年 99卷

作者： Srilakshmi, Koganti Kumar, Amit Kondreddi, Krishnaveni Krishna, T. Murali Balachandran, Praveen Kumar Gatto, Gianluca Sreenidhi Inst Sci & Technol Dept Elect & Elect Engn Hyderabad 501301 Telangana India Univ Cagliari Dept Elect & Elect Engn Via Marengo 2 I-09123 Cagliari Italy Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 Telangana India Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi Selangor Malaysia Vardhaman Coll Engn Dept Elect & Elect Engn Hyderabad Telangana India

In the present situation, the growing use of power electronic devices and non-linear loads has led to power quality (PQ) issues, including harmonics and poor power factor, which adversely affect the distribution network. This study contributes a design of shunt active power filter, powered by solar energy and energy storage systems, to address these PQ issues. To minimize losses, a five-level reduced-switch voltage source converter has been considered. Additionally, a neural network-based reference signal generation method is used, eliminating the need for conventional synchronous reference frame and active-reactive power theories, along with their complex abc and alpha beta 0 transformations. This work also includes the optimal selection of the shunt filter and the gain parameters for the proportional-integral-derivative (PID) controller used in the shunt and battery control system. These parameters, along with the weights and biases of the neural network, are optimally determined using a nature-inspired flower pollination optimization algorithm. The proposed system has three primary objectives: (1) stabilizing the voltage across the DC bus capacitor, (2) reducing total harmonic distortion (THD) and improving the power factor (PF), and (3) ensuring the power management under the varying irradiation and load conditions. The effectiveness of the proposed system is evaluated through three testing scenarios, with results compared to conventional SRF and pq methods using a proportional-integral controller (PIC). The analysis reveals that the THD for the case studies is 3.32 %, 2.93 %, and 3.98 %, significantly lower than the other techniques compared. Additionally, the PF is nearly at unity, with a lower settling time of 0.05 s for the DC bus voltage.

关键词： PID controller flower pollination optimization algorithm Energy Storage System Solar Energy System Shunt active power filter

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Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

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OPTICAL ENGINEERING 2023年第3期62卷 033105-033105页

作者： Liu, Tingting Zhang, Yanzhu Yang, Fan Shenyang Ligong Univ Sch Automat & Elect Engn Shenyang Peoples R China Sci & Technol Electroopt Informat Secur Control L Tianjin Peoples R China

The traditional fractional order total variational model has better results in denoising and maintaining texture details in infrared images. However, it is difficult to determine the order of fractional order differentiation in image processing so that the model has the best denoising effect. To solve this problem, a fractional order total variational infrared image denoising model incorporating a flower pollination particle swarm optimization (PSO) algorithm is proposed in this paper. The model combines the search advantages of the flower pollination optimization algorithm and the PSO algorithm. The maximization multiobjective equation is designed as the fitness function of the optimization algorithm. The optimal order of the fractional order total variational model is found adaptively according to different features in different regions of the infrared image. The experimental results show that the improved model not only achieves the adaptivity of the adaption of the fractional order of total variational model order but also effectively removes the noise and retains the texture structure of infrared images to the maximum extent. (c) 2023 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： fractional order total variational model infrared image denoising flower pollination optimization algorithm particle swarm optimization algorithm

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Fractional chaos maps with flower pollination algorithm for chaotic systems' parameters identification

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NEURAL COMPUTING & APPLICATIONS 2020年第20期32卷 16291-16327页

作者： Yousri, Dalia Allam, Dalia Babu, Thanikanti Sudhakar AbdelAty, Amr M. Radwan, Ahmed G. Ramachandaramurthy, Vigna K. Eteiba, M. B. Fayoum Univ Fac Engn Dept Elect Engn Al Fayyum Egypt Univ Tenaga Nas Inst Power Engn Dept Elect Power Engn Kajang Malaysia Fayoum Univ Fac Engn Engn Math & Phys Dept Al Fayyum Egypt Cairo Univ Fac Engn Engn Math & Phys Dept Giza Egypt Nile Univ Sch Engn & Appl Sci Giza Egypt

Meta-heuristic optimization algorithms are the new gate in solving most of the complicated nonlinear systems. So, improving their robustness, reliability, and convergence speed is the main target to meet the requirements of various optimization problems. In the current work, three different fractional-order chaos maps (FC-maps), which have been introduced recently, are incorporated with the fundamental flower pollination algorithm to tune its parameters adaptively. These maps are fractional logistic map, fractional sine map, fractional tent map, and their integer-order versions. As a result, fractional chaotic FPA (FC-FPA) is proposed. The FC-FPA has been mathematically tested over 10-, 30-, 50-, and 100-dimensional CEC 2017 benchmark functions. Moreover, the influence of merging FC-maps with FPA is investigated in case of increasing the number of maximum evaluation functions based on the ten functions of CEC 2020. Additionally, to assess the superiority of the proposed FC-FPA algorithm for more complicated optimization problems, it has been tested to extract the parameters of different chaotic systems with and without added noise. In addition, it is tested on the identification of the corresponding parameters for the chaotic behavior in brush-less DC motor. The results of the fractional version of CFPA are compared with that of integer CFPA and standard FPA via an extensive statistical analysis. Furthermore, a nonparametric statistical test is employed to affirm the superiority of the proposed fractional variants of CFPA. It is evident that the performance of FPA is highly influenced by integrating the fractional-order chaos maps as the introduced FC-FPA variants provide a better accurate and more consistent results as well as a higher speed of convergence especially upon using the fractional sine map.

关键词： Fractional chaos maps flower pollination optimization algorithm Chaotic systems Chaotic systems with noise

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Optimal directional digital overcurrent relays coordination and arc-flash hazard assessments in meshed networks

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2016年第1期26卷 134-154页

作者： El-Fergany, Attia Zagazig Univ Fac Engn Dept Elect Power & Machines POB 44519 Zagazig Egypt

The article addresses the arc-flash hazard assessments combining the optimal coordination of digital overcurrent relays. A novel objective function is adopted to concurrently reduce the arc-flash hazards while adapting the optimal settings of directional overcurrent relays subjects to set of coordination constraints. The flower pollination algorithm (FPA) is applied to attain the target. The proposed FPA-based technique is employed to define three main parameters for the relay settings. The performance and value of the proposed FPA-based methodology are demonstrated on the 8-bus and 15-bus meshed networks with various fault scenarios. Comparisons with the other competitive algorithms and subsequent discussions validate its good performance. Efficacious attributes of the novel objective function are compared and highlighted over the traditional objective function commonly used by the other researchers reported in the literature. Copyright (c) 2015 John Wiley & Sons, Ltd.

关键词： directional overcurrent P-based relay coordination optimal relay settings arc-flash hazard assessment incident energy flower pollination optimization algorithm

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Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy

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JOURNAL OF WATER AND CLIMATE CHANGE 2021年第6期12卷 2422-2443页

作者： Farzin, Saeed Valikhan Anaraki, Mahdi Semnan Univ Dept Water Engn & Hydraul Struct Fac Civil Engn Semnan Iran

In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination optimization algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020-2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020-2034, 2035-2049, 2070-2084, and 2085-2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036-2039 (up to an 8% increase in 2038).

关键词： climate change flower pollination optimization algorithm hybridization strategy least-squares support-vector machine suspended sediment loads

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Optimized Convolutional Neural Network-Based Adaptive Controller for 6-Phase DFIG-Based Wind Energy Systems

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第19期51卷 2262-2283页

作者： Azhakappan, Arunkumar Chellappan, Agees Kumar Sethuramalingam, Murugan Arunachala Coll Engn Women Dept Elect & Elect Engn Nagercoil India Thamirabharani Engn Coll Dept Elect & Commun Engn Tirunelveli India Arunachala Coll Engn Women Dept Elect & Elect Engn Nagercoil 629203 India

This article proposes an optimized convolutional neural network-based adaptive control scheme (OCAC) for DFIG-based wind energy conversion systems. While linear systems can function successfully with the help of a PI controller, the behavior of the system becomes unstable when physical variations are present, rendering the PI controller ineffective. The purpose of this research is to guarantee that the proposed OCAC acquires self-adaptation under all conditions. By accounting for crucial circumstances such as changes in wind speed, fluctuations in generator parameters, and asymmetrical grid faults, the efficiency of OCAC control is proven. The hyperparameters of the deep convolutional neural network are optimized using the flower pollination algorithm, which boosts the network's speed and precision. In comparison to the non-optimized technique, an overall improvement of 6.3% in training accuracy was attained through the use of the optimized method. Furthermore, the suggested OCAC would forecast the next systemic state and update control strategies of DFIG-based wind energy systems in real-time. The effectiveness of the OCAC is assessed with five different state-of-the-art algorithms under two distinct test scenarios. The simulation was conducted using MATLAB software. A comparison with a PID controller showed that the total harmonic distortion of the grid current decreased by 16.57% and that of the generator current decreased by 12.07%.

关键词： six-phase DFIG wind energy systems convolutional neural network control strategies flower pollination optimization algorithm

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