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 th...
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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 optimizationalgorithm 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.
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 t...
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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 flowerpollinationalgorithm. Here, a chaotic mapping along with Opposition-Based Learning is introduced for population initialization to form a novel Oppositional Chaotic flowerpollinationalgorithm. 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.
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 netwo...
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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.
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 differen...
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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 flowerpollination 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 optimizationalgorithm. 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)
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 ...
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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 flowerpollinationalgorithm, 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%.
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 circu...
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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).
Meta-heuristic optimizationalgorithms 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 requireme...
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Meta-heuristic optimizationalgorithms 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 flowerpollinationalgorithm 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.
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 adaptin...
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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 flowerpollinationalgorithm (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.
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