Hybridization of ALO and GOA for Combined Economic Emission Dispatch

894-897A hybrid algorithm is presented for CEED problem with generation, emission and combustion of fuel & emission cost as an objective. The proposed algorithm is combined with both ant lion optimizer and grass hopper optimizations called as integrated ant lion grasshopper optimization algorithm (IALGOA). To find an optimal solution for a CEED, the IALGOA is proposed in this paper. The IALGOA performance is compared and analyzed with conventional hybrid algorithms like PSO, GSA and Adaptive Wind Driven Optimization (AWDO) under standard IEEE 30-bus test system. The presented numerical results explain IALGOA algorithm’s excellent convergence characteristics

Hybridization of ALO and GOA for Combined Economic Emission Dispatch

A hybrid algorithm is presented for CEED problem with generation, emission and combustion of fuel & emission cost as an objective. The proposed algorithm is combined with both ant lion optimizer and grass hopper optimizations called as integrated ant lion grasshopper optimization algorithm (IALGOA). To find an optimal solution for a CEED, the IALGOA is proposed in this paper. The IALGOA performance is compared and analyzed with conventional hybrid algorithms like PSO, GSA and Adaptive Wind Driven Optimization (AWDO) under standard IEEE 30-bus test system. The presented numerical results explain IALGOA algorithm’s excellent convergence characteristics

Adaptive wind driven optimization and moth swarm algorithm in solving economic emission dispatch problem

U ovom radu su primenjeni algoritam roja noćnih leptira (MSA) i adaptivna optimizacija inspirisana vetrom (AWDO) za rešavanje nelinearnog problema ekonomične raspodele snaga (ERS) generatora u termoelektranama. Utvrđeno je da ovi algoritmi imaju visoku efikasnost u rešavanju ERS problema i izvršena je statistička analiza ponašanja ovih algoritama. Algoritmi MSA i AWDO su testirani na standardnim IEEE test sistemima sa 3 i 6 generatora i pokazali su bolje performanse u odnosu na algoritme primenjivane u publikovanoj literaturi. Zatim je problem ERS proširen problemom lanca snabdevanja električnom energijom na deregulisanom tržištu pa je takav integrisani problem rešavan primenom AWDO. Na rezultate dobijene testiranjem algoritama primenjeni su statistički parametarski i neparametarski testovi kako bi se utvrdila razlika u ponašanju algoritama pri dobijanju rezultata na pojedinačnim funkcijama ERS problema i na svim funkcijama zajedno i kako bi se utvrdilo da li se mogu generalizovati zaključci iz konkretnih skupova rešenja na celu populaciju mogućih rešenja. Rezultati statističke analize su pokazali da se algoritmi ponašaju različito za različite funkcije ERS problema tj., da jedan algoritam ne može biti najbolji za svaku funkciju. To znači da je pri rešavanju problema koji se sastoji od većeg broja funkcija bolje primeniti veći broj odgovarajućih algoritama umesto jednog

An improved jellyfish algorithm for multilevel thresholding of magnetic resonance brain image segmentations

Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based onmultilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer).We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is achieved by applying two novel strategies: Rankingbased updating and an adaptive method. Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions. We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution; we allow a small amount of exploration to avoid descents into local minima. The two strategies are integrated with the JSA to produce an improved JSA (IJSA) that optimally thresholds brain MR images. To compare the performances of the IJSA and JSA, seven brain MR images were segmented at threshold levels of 3, 4, 5, 6, 7, 8, 10, 15, 20, 25, and 30. IJSA was compared with several other recent image segmentation algorithms, including the improved and standard marine predator algorithms, the modified salp and standard salp swarm algorithms, the equilibrium optimizer, and the standard JSA in terms of fitness, the Structured Similarity Index Metric (SSIM), the peak signal-to-noise ratio (PSNR), the standard deviation (SD), and the Features Similarity IndexMetric (FSIM). The experimental outcomes and theWilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM, the PSNR, the objective values, and the SD; in terms of the SSIM, IJSA was competitive with the others.</p

An improved wind driven optimization algorithm for parameters identification of a triple-diode photovoltaic cell model

© 2020 Elsevier Ltd The double-diode photovoltaic cell model is insufficient to accurately characterize the different current components of a photovoltaic cell. Therefore, the triple-diode model of a photovoltaic cell is considered to model its complicated physical characteristics by clearly defining the different current components of the photovoltaic cell. The identification of its unknown parameters is a complex, multi-modal and multi-variable optimization problem. An improved wind driven optimization algorithm is proposed in this paper to identify its nine unknown parameters. The proposed method is a combination of the mutation strategy of the differential evolution algorithm and the covariance matrix adaptation evolution strategy of the wind driven optimization algorithm. The mutation strategy aims to bolster the exploration ability of the improved wind driven optimization algorithm, while the covariance matrix adaptation evolution strategy based on wind driven optimization algorithm aims to improve the searching of the classical wind driven optimization algorithm. Therefore, improved wind driven optimization algorithm is more accurate and faster than the classical wind driven optimization algorithm in finding the global optimum and balancing exploration and exploitation. The proposed model has been utilized on 15-minute interval data to identify the unknown parameters of three commercial photovoltaic technologies, namely, mono-crystalline, poly-crystalline and thin-film. To show the effectiveness of the proposed model, its performance is validated by comparing it with that obtained by the classical wind driven optimization, the adaptive wind driven optimization, moth-flame optimizer, sunflower optimization and the improved opposition-based whale optimization algorithms. The results demonstrate that improved wind driven optimization outperforms the aforementioned models in accuracy, convergence speed and feasibility. In addition, improved wind driven optimization more clearly defined different current components and generated any current-voltage curve under any operating condition

Deep Learning Model Based on ResNet-50 for Beef Quality Classification

Food quality measurement is one of the most essential topics in agriculture and industrial fields. To classify healthy food using computer visual inspection, a new architecture was proposed to classify beef images to specify the rancid and healthy ones. In traditional measurements, the specialists are not able to classify such images, due to the huge number of beef images required to build a deep learning model. In the present study, different images of beef including healthy and rancid cases were collected according to the analysis done by the Laboratory of Food Technology, Faculty of Agriculture, Kafrelsheikh University in January of 2020. The texture analysis of the beef surface of the enrolled images makes it difficult to distinguish between the rancid and healthy images. Moreover, a deep learning approach based on ResNet-50 was presented as a promising classifier to grade and classify the beef images. In this work, a limited number of images were used to present the research problem of image resource limitation; eight healthy images and ten rancid beef images. This number of images is not sufficient to be retrained using deep learning approaches. Thus, Generative Adversarial Network (GAN) was proposed to augment the enrolled images to produce one hundred eighty images. The results obtained based on ResNet-50 classification achieve accuracy of 96.03%, 91.67%, and 88.89% in the training, testing, and validation phases, respectively. Furthermore, a comparison of the current model (ResNet-50) with the classical and deep learning architecture is made to demonstrate the efficiency of ResNet-50, in image classification

A New FL-MPPT High Voltage DC-DC Converter for PV Solar Application

To reduce the effects of global warming, there is an increasing need for renewable energy sources. Several studies have been carried out on photovoltaic (PV) systems to maximize their potential as an alternative electricity generator. However, various power converters for high voltage ratio applications have multiple drawbacks. This research was carried out to develop a power converter topology connected between the PV and the load for the need. In this research, the high step-up DC-DC converter for high-voltage gain conversion ratio and high efficiency is proposed. Furthermore, the fuzzy logic-based Maximum Power Point Tracking (MPPT) technique connected to the power converter was used to maximize the power converted from PV in changing atmospheric conditions. The MPPT control with fuzzy logic controller (FLC) was analysed and compared with the perturb and observe (P&O) algorithm. The results showed that the FLC algorithm could contro