Interval prediction of electric load has aroused widespread concern by the power industry because of variability and uncertainty. To quantify the potential uncertainty associated with prediction, this paper proposes a...
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Interval prediction of electric load has aroused widespread concern by the power industry because of variability and uncertainty. To quantify the potential uncertainty associated with prediction, this paper proposes a clustering-based approach to construct prediction intervals (PIs) for electric load data. The singular spectrum analysis (SSA) and k-means clustering are firstly performed to decompose the original data due to the high volatility and nonlinearity of load data. Then, we improve the multi-objective pathfinder algorithm (MOPATH) by using crowding degree of population in order to prevent premature, and further utilize the Elman neural network (ELMAN) optimized by IMOPATH to obtain the subseries PIs of electric load data. In addition, the interval width, coverage probability and deviation are used as three optimization objectives. Finally, the IMOPATH, as an ensemble approach, is applied to ensemble the three PIs together and achieves the final PIs. To verify the performance of the SSA-IMOPATH-ELMAN approach, the proposed approach is compared with 41 models. The forecasting outcomes indicate that PIs of the proposed approach have higher coverage probability, narrower width and lower deviation degree than other benchmark models. Moreover, the proposed approach has good performance on robustness and sensibility. (c) 2022 Elsevier B.V. All rights reserved.
This paper presents a multi-objective version of the artificial vultures optimization algorithm (AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA). The inspirational concept of th...
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This paper presents a multi-objective version of the artificial vultures optimization algorithm (AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA). The inspirational concept of the AVOA is based on African vultures' lifestyles. Archive, grid, and leader selection mechanisms are used for developing the MOAVOA. The proposed MOAVOA algorithm is tested oneight real-world engineering design problems and seventeen unconstrained and constrained mathematical optimization problems to investigates its appropriateness in estimating Pareto optimal solutions. Multi-objective particle swarm optimization, multi-objective ant lion optimization, multi-objective multi-verse optimization, multi-objective genetic algorithms, multi-objective salp swarm algorithm, and multi-objective grey wolf optimizer are compared with MOAVOA using generational distance, inverted generational distance, maximum spread, and spacing performance indicators. This paper demonstrates that MOAVOA is capable of outranking the other approaches. It is concluded that the proposed MOAVOA has merits in solving challenging multi-objective problems.
Purpose Cloud eases information processing, but it holds numerous risks, including hacking and confidentiality problems. It puts businesses at risk in terms of data security and compliance. This paper aims to maximize...
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Purpose Cloud eases information processing, but it holds numerous risks, including hacking and confidentiality problems. It puts businesses at risk in terms of data security and compliance. This paper aims to maximize the covered human resource (HR) vulnerabilities and minimize the security costs in the enterprise cloud using a fuzzy-based method and firefly optimization algorithm. Design/methodology/approach Cloud computing provides a platform to improve the quality and availability of IT resources. It changes the way people communicate and conduct their businesses. However, some security concerns continue to derail the expansion of cloud-based systems into all parts of human life. Enterprise cloud security is a vital component in ensuring the long-term stability of cloud technology by instilling trust. In this paper, a fuzzy-based method and firefly optimization algorithm are suggested for optimizing HR vulnerabilities while mitigating security expenses in organizational cloud environments. MATLAB is employed as a simulation tool to assess the efficiency of the suggested recommendation algorithm. The suggested approach is based on the firefly algorithm (FA) since it is swift and reduces randomization throughout the lookup for an optimal solution, resulting in improved performance. Findings The fuzzy-based method and FA unveil better performance than existing met heuristic algorithms. Using a simulation, all the results are verified. The study findings showed that this method could simulate complex and dynamic security problems in cloud services. Practical implications The findings may be utilized to assist the cloud provider or tenant of the cloud infrastructure system in taking appropriate risk mitigation steps. Originality/value Using a fuzzy-based method and FA to maximize the covered HR vulnerabilities and minimize the security costs in the enterprise cloud is the main novelty of this paper.
Analog circuit design is comparatively more complex than its digital counterpart due to its nonlinearity and low level of abstraction. This study proposes a novel low-level hybrid of the sine-cosine algorithm (SCA) an...
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Analog circuit design is comparatively more complex than its digital counterpart due to its nonlinearity and low level of abstraction. This study proposes a novel low-level hybrid of the sine-cosine algorithm (SCA) and modified grey-wolf optimization (mGWO) algorithm for machine learning-based design automation of CMOS analog circuits using an all-CMOS voltage reference circuit in 40-nm standard process. The optimization algorithm's efficiency is further tested using classical functions, showing that it outperforms other competing algorithms. The objective of the optimization is to minimize the variation and power usage, while satisfying all the design limitations. Through the interchange of scripts for information exchange between two environments, the SCA-mGWO algorithm is implemented and simultaneously simulated. The results show the robustness of analog circuit design generated using the SCA-mGWO algorithm, over various corners, resulting in a percentage variation of 0.85%. Monte Carlo analysis is also performed on the presented analog circuit for output voltage and percentage variation resulting in significantly low mean and standard deviation.
A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes...
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A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search algorithm (HSSOGSA). The results are evaluated in terms of each method's best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.
In this study, we present a novel algorithm that combines a rule-based approach and an artificial neural network-based approach in morphological analysis. The usage of hybrid models including both techniques is evalua...
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In this study, we present a novel algorithm that combines a rule-based approach and an artificial neural network-based approach in morphological analysis. The usage of hybrid models including both techniques is evaluated for performance improvements. The proposed hybrid algorithm is based on the idea of the dynamic generation of an artificial neural network according to two-level phonological rules. In this study, the combination of linguistic parsing, a neural network-based error correction model, and statistical filtering is utilized to increase the coverage of pure morphological analysis. We experimented hybrid algorithm applying rule-based and long short-term memory-based (LSTM-based) techniques, and the results show that we improved the morphological analysis performance for optical character recognizer (OCR) and social media data. Thus, for the new hybrid algorithm with LSTM, the accuracy reached 99.91% for the OCR dataset and 99.82% for social media data.
Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational *** with other types of algorithms,in metaheuristic algor...
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Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational *** with other types of algorithms,in metaheuristic algorithms,one of the methods used to improve performance and achieve results closer to the target result is the hybridization of *** this study,a hybrid algorithm(HSSJAYA)consisting of salp swarm algorithm(SSA)and jaya algorithm(JAYA)is *** speed of achieving the global optimum of SSA,its simplicity,easy hybridization and JAYA’s success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two *** hybrid algorithm is based on SSA’s leader and follower salp system and JAYA’s best and worst solution *** works according to the best and worst food source *** this way,it is thought that the leader-follower salps will find the best solution to reach the food *** hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark *** results were compared with SSA,JAYA,cuckoo search algorithm(CS),firefly algorithm(FFA)and genetic algorithm(GA).As a result,a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was *** addition,these results were statistically compared using wilcoxon rank sum test with other *** to the statistical results obtained from the results of the benchmark functions,it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.
Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization *** optimization problems,metaheuristic algorithm is one of the methods to find its optimal solution or...
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Metaheuristic algorithm is a generalization of heuristic algorithm that can be applied to almost all optimization *** optimization problems,metaheuristic algorithm is one of the methods to find its optimal solution or approximate solution under limited *** of the existing metaheuristic algorithms are designed for serial ***,existing algorithms still have a lot of room for improvement in convergence speed,robustness,and *** address these issues,this paper proposes an easily parallelizable metaheuristic optimization algorithm called team competition and cooperation optimization(TCCO)inspired by the process of human team cooperation and *** proposed algorithm attempts to mathematically model human team cooperation and competition to promote the optimization process and find an approximate solution as close as possible to the optimal solution under limited *** order to evaluate the performance of the proposed algorithm,this paper compares the solution accuracy and convergence speed of the TCCO algorithm with the Grasshopper Optimization algorithm(GOA),Seagull Optimization algorithm(SOA),Whale Optimization algorithm(WOA)and Sparrow Search algorithm(SSA).Experiment results of 30 test functions commonly used in the optimization field indicate that,compared with these current advanced metaheuristic algorithms,TCCO has strong competitiveness in both solution accuracy and convergence speed.
There is an increasing trend for fuel cell systems applications in electricity generation systems instead of traditional power generation systems because of their advantages such as high efficiency and almost no envir...
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There is an increasing trend for fuel cell systems applications in electricity generation systems instead of traditional power generation systems because of their advantages such as high efficiency and almost no environmental pollution, desirable dynamic response, and reliability. Due to this reason, herein, a new method has been presented for optimum identification of the model of the proton exchange membrane fuel cell (PEMFC) model. The major concept is to lessen the sum of squared error (SSE) amount of the observed output voltage and the output voltage of the PEMFC stack by an improved version of Crow Search optimizer (ICSO). To validate the suggested technique, it is applied to two studied cases and the achievements are put in comparison with several newest optimizers, which are Ge-netic algorithm (GA), Grasshopper Optimizer (GHO), and Salp Swarm Optimizer (SSO). The achievements show that the suggested ICSO gives a better superiority to the other comparative algorithms for optimum estimation of the PEMFC model.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Model parameters identification of the fuel cells has several applications in practice. However, due to its nonlinear dynamic and its complicated nature, solving it based on most of the classic methods is too hard. Al...
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Model parameters identification of the fuel cells has several applications in practice. However, due to its nonlinear dynamic and its complicated nature, solving it based on most of the classic methods is too hard. Also, using any kind of methods based on soft computing does not also provide a satisfying result which can be proved based on the no free lunch theorem. In the present study, a new methodology has been proposed for optimum parameters identification of the Proton exchange membrane fuel cell (PEMFC) stacks. Here, an improved version of African vulture optimizer (IAVO) is organized for this purpose. To prove the accuracy of the proposed method, it is applied to three standard test cases and the results have been compared with some five latest techniques. The results showed that the proposed IAVO algorithm with 1.98(+/- 0.49), 0.03(+/- 0.02), and 1.08(+/- 0.47) parameter fitting value for NedStackPS6, BCS 500 W, and SR-12 500 W, respectively, provides the best results with minimum error value. This shows that the efficiency of the proposed IAVO algorithm is too better than the other methods in parameter identification of the PEMFC stacks.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
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