Optimal multilevel thresholding for image segmentation got much importance in recent years. Several entropic and non-entropic objective functions with evolutionary computing algorithms have been successfully implement...
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Optimal multilevel thresholding for image segmentation got much importance in recent years. Several entropic and non-entropic objective functions with evolutionary computing algorithms have been successfully implemented to get the optimal multilevel thresholds for gray scale images. The problem of multilevel thresholding becomes complex for color images. Because, the basic color components (red, blue, green) of the color image are extracted and the multiple optimum threshold values are calculated for each of the components separately. This makes the methods computationally intensive and inaccurate. Further, the required color information is not retained in the thresholded output. To solve these problems, an efficient technique is proposed in this paper, extracting only the dominant color component (DCC) of an image, for optimal thresholding. A novel segmentation score is introduced to justify the methodology. The optimum threshold values are obtained using a newly suggested evolutionary computing technique named adaptive whale optimization algorithm (AWOA). The main contributions are - (i) a novel DCC approach is introduced, (ii) an efficient optimizer AWOA is proposed, (iii) a new segmentation score is introduced, (iv) experimental results on standard test color images are explored. The outcomes are compared with all existing method's approaches (using all the RGB components) on color image thresholding. Its performance analysis using standard metrics is deliberated in detail. Statistical analysis is also performed. From the outcomes, it is perceived that the suggested DCC-AWOA concept yields high quality segmented images. The work may encourage further research to explore its high dimensional applications. (C)& nbsp;& nbsp;2022 Elsevier B.V. All rights reserved.& nbsp
Precise prediction of carbon prices by means of single forecasting models may be difficult due to the inherent non-stationary and nonlinearity characteristics of the carbon price. This paper i proposes an innovative h...
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Precise prediction of carbon prices by means of single forecasting models may be difficult due to the inherent non-stationary and nonlinearity characteristics of the carbon price. This paper i proposes an innovative hybrid model for predicting the carbon price. The prediction was made through the extreme learning machine optimized by the adaptive whale optimization algorithm based on the multi-resolution singular value decomposition. The multi-resolution singular value decomposition was used to eliminate the high frequency components of the previous carbon price data. Afterwards, the carbon price was successfully decomposed into two time series the approximation series and the detailed series. The partial auto-correlation function was employed in the approximation series for determining the input variables of the extreme learning machine. The adaptive whale optimization algorithm was utilized to optimize both the input weight matrix and the bias matrix to improve the robustness and accuracy of extreme learning machines. The empirical simulation based on four diverse types of carbon prices under carbon trading pilot programs in China found that the proposed model outperformed the other benchmark methods. Four different matured carbon future prices under the European Union national emissions trading scheme (EU ETS) were also selected for forecasting. The results showed that the proposed model performed fairly well in forecasting the EU carbon price.
An optimal utilization of smart grid connected hybrid renewable energy sources is proposed in this paper. The hybrid technique is the combination of recurrent neural network and adaptive whale optimization algorithm p...
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An optimal utilization of smart grid connected hybrid renewable energy sources is proposed in this paper. The hybrid technique is the combination of recurrent neural network and adaptive whale optimization algorithm plus tabu search, called AWOTS. The main objective is the RES optimum operation for decreasing the electricity production cost by hourly day-ahead and real time scheduling. Here, the load demands are predicted using AWOTS to develop the correct control signals based on power difference between source and load side. adaptive whale optimization algorithm searching behaviour is adjusted by tabu search. The proposed technique is executed in the MATLAB/Simulink working platform. To test the performance of the proposed method, the load demand for the 24-hour time period is demonstrated. By then the power generated in the sources, such as photovoltaic, wind turbine, micro turbine and battery by the proposed technique, is analyzed and compared with existing techniques, such as genetic algorithm, particle swarm optimization and whaleoptimizationalgorithm. Furthermore, the state of charge of the battery for the 24-hour period is compared with existing techniques. Likewise, the cost of the system is compared and error in the sources also compared. The comparison results affirm that the proposed technique has less computational time (35.001703) than the existing techniques. Moreover, the proposed method is cost-effective power production of smart grid and effective utilization of renewable energy sources without wasting the available energy.
In order to extract the non-linear fault characteristics of rolling bearings more accurately, a novel nonlinear dynamical analysis method, referred to as the refined composite multi-scale dispersion q-complexity (RCMS...
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In order to extract the non-linear fault characteristics of rolling bearings more accurately, a novel nonlinear dynamical analysis method, referred to as the refined composite multi-scale dispersion q-complexity (RCMSDQC), is proposed for fault feature extraction of rolling bearings. To improve further the overall performance of the extreme learning machine (ELM) algorithm, the adaptive whale optimization algorithm (AWOA) is used to determine the input weights and hidden layer biases of the ELM. The RCMSDQC has the advantages of strong feature extraction ability and stability compared to the composite multi-scale weighted permutation entropy (CMSWPE) and composite multi-scale permutation entropy (CMSPE) methods. Furthermore, compared to the whaleoptimizationalgorithm, particle swarm optimization, and genetic algorithm, the AWOA shows a superior performance in the benchmark function comparison experiment. Based on the experimental rolling bearing data from the Paderborn University, the performance of the proposed method is further evaluated. The experimental results indicate that the proposed fault diagnosis method can identify the type and severity of rolling bearing faults with an accuracy of 99.1%.
In this paper, a variant of the recently introduced whaleoptimizationalgorithm (WOA) was proposed based on adaptive switching of random walk per individual search agent. WOA is recently proposed bio-inspired optimiz...
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ISBN:
(纸本)9781538633687
In this paper, a variant of the recently introduced whaleoptimizationalgorithm (WOA) was proposed based on adaptive switching of random walk per individual search agent. WOA is recently proposed bio-inspired optimizers that employ two different random walks. The original optimizer stochastically switches between the two random walk at each iteration regardless of the search agents performance and regardless of the fitness terrain around it. In the proposed adaptive walk whaleoptimizationalgorithm (AWOA), an adaptive switching between the two random walk is recommended based on the agent's performance. Moreover, a random explorative switch of the walk is applied to allow search agents to try different walks. The proposed AWOA was benchmarked using 29 standard test functions with uni-modal, multi-modal, and composite test functions. Performance over such functions proves the capability of the proposed variant to outperform the original WOA.
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