Various optimization objective functions have been proposed for harmonic optimization problems to eliminate unwanted specific harmonic or reduce total harmonic distortion (THD). However, the objective functions are us...
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ISBN:
(纸本)9781728151359
Various optimization objective functions have been proposed for harmonic optimization problems to eliminate unwanted specific harmonic or reduce total harmonic distortion (THD). However, the objective functions are usually intuitively selected, which lack a theoretical basis. In this paper, five objective functions with different mathematical forms are evaluated by two commonly used bio-inspired optimization algorithms, namely the particle swarm optimizers (PSO), and differential evolution (DE). By comparing the THDs, the low-order individual harmonics, and the computational time, resulted from the five objective functions, some guidelines are summarized for selecting objective functions for the bio-inspired harmonic optimizationalgorithms.
The network-on-chip (NoC) mapping confronts a balance between energy consumption, area and the performance of system-on-chip (SoC) in order to achieve optimum performance. In this article, we want to solve a two-objec...
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ISBN:
(纸本)9781479979585
The network-on-chip (NoC) mapping confronts a balance between energy consumption, area and the performance of system-on-chip (SoC) in order to achieve optimum performance. In this article, we want to solve a two-objective optimization problem in terms of NoC energy consumption and communication latency by optimizing distribution of link load. Traditional heuristic approaches such as genetic algorithm are likely to trap into local optimal solutions. We present a new application specific multi-objective mapping algorithm based on bio-inspired optimization algorithms combining membrane computing and conventional genetic algorithms. Then this approach is applied to two real applications with different numbers of cores. Experimental results demonstrate that the represented mapping is a feasible way to obtain better NoC architecture in term of energy consumption, latency and traffic balance than mapping based on genetic algorithm.
This study proposes a novel combined filter accompanied with different optimizationalgorithms for Poisson noise reduction and increases image quality in digital X-ray and CT images. This filter uses 4th-order PDE, TV...
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This study proposes a novel combined filter accompanied with different optimizationalgorithms for Poisson noise reduction and increases image quality in digital X-ray and CT images. This filter uses 4th-order PDE, TV, Bayes shrink threshold with optimizationalgorithms and an exact unbiased inverse of generalized Anscombe transformation (EUIGAT). Experiments were conducted on the basis of displaying the influence of denoising filter on 105 simulated, 102 radiographic and 102 CT images of individuals aged 20-70 years old;53 men and 49 women. Experimental results demonstrated the lowest value for MSE and the highest values for PSNR, IQI, SSIM, FOM and CNR in different kinds of kernels and images compared with the other fuzzy bio-inspiredalgorithms. The results showed proposed method helps physicians and orthopedists in order to enhance their performances in treating injuries of the pelvic region such as acetabulum fossa and head and neck femur bone.
This paper presents a recent bio-inspiredoptimization algorithm for the critical topic of load frequency control (LFC) in an isolated multi-source power generating system. A specific model that emerges from the scien...
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This paper presents a recent bio-inspiredoptimization algorithm for the critical topic of load frequency control (LFC) in an isolated multi-source power generating system. A specific model that emerges from the scientific literature and consists of reheated thermal, hydro and gas-turbine power sources is examined. The main goal is to tune the controller of each generating source i.e. find their optimal gains through bio-inspired optimization algorithms. Namely, the algorithms applied are the Whale optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), Particle Swarm optimization (PSO), and the newly proposed Harris Hawks optimization (HHO). It was observed that all optimizationalgorithms succeeded in obtaining controllers' gain values which ensure stable system operation. Also, the simulation results indicated the superiority of the proposed HHO algorithm over the other algorithms for all the considered scenarios. In this article, for the first time to the authors' knowledge extend an attempt has been made to optimize an isolated multi-source system using the bio-inspired HHO algorithm. The present study considered the LFC problem with a variety of different scenarios, taking into account both sub-cases of nonlinearities (e.g., generation rate constraint, boiler dynamics, etc.) and their combinations, as well as combinations of different controllers and load disturbances, which are not found in the literature. Last but not least, the robustness of the selected controller was further evaluated. The obtained results clearly demonstrated that the controller's gains established in normal conditions do not require retuning when critical system parameters undergo a significant variation.
Underwater Wireless Sensor Networks (UWSN) is one of the most emerging branches of wireless sensor networks. They are used extensively in environmental monitoring, oceanography, and marine biology. Energy efficiency, ...
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Software fault prediction is a crucial task, especially with the rapid improvements in software technology and increasing complexity of software. As identifying and addressing bugs early in the development process can...
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Software fault prediction is a crucial task, especially with the rapid improvements in software technology and increasing complexity of software. As identifying and addressing bugs early in the development process can significantly minimize the costs and enhance the software quality. Software fault prediction using machine learning algorithms has gained significant attention due to its potential to improve software quality and save time in the testing phase. This research paper investigates the impact of classification models on bug prediction performance and explores the use of bio-inspiredoptimization techniques to enhance model results. Through experiments, it is demonstrated that applying bio-inspiredalgorithms improves the accuracy of fault prediction models. The evaluation is based on multiple performance metrics and the results show that KNN with BACO (Binary Ant Colony optimization) generally outperform the other models in terms of accuracy. The BACO-KNN fault prediction model attains the accuracy of 96.39% surpassing the previous work.
Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-F...
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Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008-2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15-25), [25-35) and >= 35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under- or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set.
In recent years, research on stochastic optimizationalgorithms has received more and more attention from researchers, especially bio-inspired optimization algorithms. Selfish herd optimizer (SHO) is a novel bio-inspi...
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In recent years, research on stochastic optimizationalgorithms has received more and more attention from researchers, especially bio-inspired optimization algorithms. Selfish herd optimizer (SHO) is a novel bio-inspiredoptimization algorithm. It has features that are easy to understand and implement. However, its global search ability is insufficient and precision needs to be further improved. Therefore, we add levy-flight distribution strategy to improve its global search ability and precision. Our main contribution is to use selfish herd optimizer with levy-flight distribution strategy (LFSHO) to solve function and engineering example optimization problem. From experiment results, we can see that LFSHO has more advantages than other algorithms, according to precision, convergence speed and standard variance. It can conclude that LFSHO is a new method for solving function optimization problem and engineering example optimization problem. (C) 2019 Elsevier B.V. All rights reserved.
Actual evapotranspiration (AET) is one of the decisive factors controlling the water balance at the catchment level, particularly in arid and semi-arid regions, but measured data for which are generally unavailable. I...
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Actual evapotranspiration (AET) is one of the decisive factors controlling the water balance at the catchment level, particularly in arid and semi-arid regions, but measured data for which are generally unavailable. In this study, performance of a base artificial intelligence (AI) model, adaptive neuro-fuzzy inference system (ANFIS), and its hybrids with two bio-inspired optimization algorithms, namely shuffled frog leaping algorithm (SFLA) and grey wolf optimization (GWO), in estimating monthly AET was evaluated over 2001-2010 across Neishaboor watershed in Iran. The inputs of these models were categorized into three groups including meteorological, remotely sensed, and hybrid-based predictors, and defined in the form of 8 different scenarios. Net radiation (Rn), land surface temperature (LST), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and soil wetness deficit index (SWDI) were the remotely sensed predictors, computed using MODIS satellite images on the monthly scale for the study area. The results showed that the SWDI predictor has played a significant role in improving the accuracy of AET estimation, with the highest error reduction (12.5, 17 and 26.5% for ANFIS, ANFIS-SFLA, and ANFIS-GWO, respectively) obtained under scenarios including SWDI compared to corresponding scenarios excluding this predictor. In testing set, the three aforementioned models exhibited their best performance under Scenario 8 (RMSE = 11.93, NSE = 0.69, RRMSE = 0.37), Scenario 4 (RMSE = 11.06, NSE = 0.74, RRMSE = 0.37) and Scenario 4 (RMSE = 10.9, NSE = 0.76, RRMSE = 0.36), respectively. Coupling the SFLA and GWO optimizationalgorithms to the base model improved the accuracy of AET estimation, with the maximum error reduction for the two algorithms being about 12% (Scenarios 2 and 4) and 14% (Scenario 4), respectively. Examining the performance of the best scenarios of the three models in three intervals including the first, middle, and last thir
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