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A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms

Information Processing in Agriculture 2018年第1期5卷 162-172页

作者： Sajad Sabzi Yousef Abbaspour-Gilandeh Ginés García-Mateos Department of Biosystems Engineering College of AgricultureUniversity of Mohaghegh ArdabiliArdabilIran Computer Science and Systems Department University of Murcia30100 EspinardoMurciaSpain

Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely *** paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges(Citrus sinensis L.),namely Bam,Payvandi and Thomson.A total of 300 color images were used for the experiments,100 samples for each orange variety,which are publicly *** segmentation,263 parameters,including texture,color and shape features,were extracted from each sample using image *** them,the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm(ANN-PSO).Then,three different classifiers were applied and compared:hybrid artificial neural network–artificial bee colony(ANN-ABC);hybrid artificial neural network–harmony search(ANN-HS);and k-nearest neighbors(kNN).The experimental results show that the hybrid approaches outperform the results of *** average correct classification rate of ANN-HS was 94.28%,while ANN-ABS achieved 96.70%accuracy with the available data,contrasting with the 70.9%baseline accuracy of ***,this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties,which can be easily implemented in processing *** main contribution of this work is that the method can be directly adapted to other use cases,since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.

关键词： Computer vision Fruits classification Hybrid neural networks Image processing metaheuristic algorithms

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Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility

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SCIENTIFIC REPORTS 2024年第1期14卷 1-24页

作者： Ajin, Rajendran Shobha Segoni, Samuele Fanti, Riccardo Univ Florence UNIFI Dept Earth Sci DST I-50121 Florence Italy

In this study, a landslide susceptibility assessment is performed by combining two machine learning regression algorithms (MLRA), such as support vector regression (SVR) and categorical boosting (CatBoost), with two population-based optimization algorithms, such as grey wolf optimizer (GWO) and particle swarm optimization (PSO), to evaluate the potential of a relatively new algorithm and the impact that optimization algorithms can have on the performance of regression models. The Kerala state in India has been chosen as the test site due to the large number of recorded incidents in the recent past. The study started with 18 potential predisposing factors, which were reduced to 14 after a multi-approach feature selection technique. Six susceptibility models were implemented and compared using the machine learning algorithms alone and combining each of them with the two optimization algorithms: SVR, CatBoost, SVR-PSO, CatBoost-PSO, SVR-GWO, and CatBoost-GWO. The resulting maps were validated with an independent dataset. The performance rankings, based on the area under the receiver operating characteristic curve (AUC) metric, are as follows: CatBoost-GWO (AUC = 0.910) had the highest performance, followed by CatBoost-PSO (AUC = 0.909), CatBoost (AUC = 0.899), SVR-GWO (AUC = 0.868), SVR-PSO (AUC = 0.858), and SVR (AUC = 0.840). Other validation statistics corroborated these outcomes, and the Friedman and Wilcoxon-signed rank tests verified the statistical significance of the models. Our case study showed that CatBoost outperformed SVR both in case the models were optimized or not;the introduction of optimization algorithms significantly improves the results of machine learning models, with GWO being slightly more effective than PSO. However, optimization cannot drastically alter the results of the model, highlighting the importance of setting up of a rigorous susceptibility model since the early steps of any research.

关键词： Kerala Landslide susceptibility Machine learning metaheuristic algorithms Regression Western Ghats

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metaheuristic algorithms for calibration of two-dimensional wildfire spread prediction model

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期136卷

作者： Pereira, Jorge Mendes, Jerome Junior, Jorge S. S. Viegas, Carlos Paulo, Joao Ruivo Univ Coimbra Inst Syst & Robot Dept Elect & Comp Engn ARISE Coimbra Portugal Univ Coimbra Dept Mech Engn CEMMPRE ARISE Coimbra Portugal Univ Coimbra ADAI Dept Mech Engn Rua Luis Reis SantosPolo 2 P-3030788 Coimbra Portugal

Wildfires are complex phenomena with harmful consequences, ranging from environmental and property destruction to loss of human lives. In this sense, predicting wildfire behaviour is essential to mitigate its impacts and consequences. The Rothermel model is the most used fire rate of spread prediction model. However, input parameter uncertainty is a significant source of prediction error. In this paper, we propose the calibration of the input parameters of the fire propagation model by metaheuristic algorithms under a two-stage framework. The fire spread model consists on the Rothermel model in a two-dimensional approach for surface fires. The proposed calibration is performed in two stages iteratively repeated over time: (i) the calibration of the fire spread model's input parameters and (ii) the wildfire spread prediction using the calibrated input parameters. The calibration was performed by the genetic algorithm, differential evolution, and simulated annealing, which calibrates the surface-area-to-volume ratio, fuel bed depth, live fuel moisture and dead fuel moisture. The symmetric difference between the real and predicted fire map shapes was defined as the fitness function of all three metaheuristic algorithms. For validation, simulations were done on two prescribed fires. The results for the real and estimated fire behaviour were then compared and revealed that all the tested metaheuristic algorithms produce a better fit to the real fire's perimeter when compared to the uncalibrated Rothermel model. From the results, differential evolution provided the majority of best results when compared to genetic algorithm and simulated annealing algorithms in each scenario.

关键词： metaheuristic algorithms Wildfire spread prediction Model calibration Remote sensing

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metaheuristic algorithms for Convolution Neural Network

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016年第1期2016卷 1537325-1537325页

作者： Rere, L. M. Rasdi Fanany, Mohamad Ivan Arymurthy, Aniati Murni Univ Indonesia Fac Comp Sci Machine Learning & Comp Vis Lab Depok 16424 Indonesia STMIK Jakarta STI&K Comp Syst Lab Jakarta 12140 Indonesia

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).

关键词： metaheuristic algorithms NEURAL networks (Computer science) DEEP learning (Machine learning) ARTIFICIAL intelligence SIMULATED annealing (Mathematics)

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Combining Machine Learning & metaheuristic algorithms for Predicting Waiting Time of High Performance Computing jobs 5

Combining Machine Learning & Metaheuristic Algorithms for Pr...

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5th International Conference on Innovative Trends in Information Technology (ICITIIT)

作者： Ramachandran, Suja Jayalal, M. L. Vasudevan, M. Jehadeesan, R. Indira Gandhi Ctr Atom Res Kalpakkam Tamil Nadu India

ISBN: (纸本)9798350386813;9798350386820

We have combined a Machine Learning model (Extreme Gradient Boosting - XGBoost) and metaheuristic algorithms to predict the time which a job submitted to a High Performance Computing (HPC) cluster will have to spend waiting in a queue. Historical data on previous jobs, status of cluster, queue and pending jobs at the time of job submission and job specific details are extracted as inputs to the prediction model. Three metaheuristic algorithms, namely, Genetic algorithms (GA), Particle Swarm Optimization (PSO) and Dragon Fly Algorithm (DFA) are implemented to optimize feature selection, fine-tune model parameters, and determine the most suitable data normalization technique. The objective is to minimize prediction errors, improve accuracy of predicting jobs that start immediately (NIL waiting times), and reduce model training time. Our experimental results demonstrate the efficacy of metaheuristic algorithms in optimizing multiple objectives, outperforming baseline models significantly.

关键词： XGBoost Machine Learning metaheuristic algorithms Waiting Time Prediction HPC

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metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第24期36卷

作者： Mohanty, Sagarika Sahoo, Bibhudatta Natl Inst Technol Dept Comp Sci & Engn Rourkela 769008 Odisha India

Software-defined networking (SDN) has revolutionized network architectures by decoupling the control plane from the data plane. An intriguing challenge within this paradigm is the strategic placement of controllers and the allocation of switches to optimize network performance and resilience. In the event of a controller failure, the switches are disconnected from the controller until they are reassigned to other active controllers possessing sufficient spare capacity. The reassignment could lead to a significant rise in propagation latency. This correspondence presents a mathematical model for capacitated controller placement, strategically designed to anticipate failures and prevent a substantial increase in worst-case latency and disconnections. The aim is to minimize the worst-case latency between switches and their backup controllers and among the controllers. Four metaheuristic algorithms are proposed including, an enhanced genetic algorithm (CCPCFR-EGA), particle swarm optimization (CCPCFR-PSO), a hybrid particle swarm optimization and simulated annealing algorithm (CCPCFR-HPSOSA), and a grey wolf optimization algorithm (CCPCFR-GWO). These algorithms are compared with a simulated annealing method and an optimal method. Evaluation conducted on four network datasets demonstrates that the proposed metaheuristic methods are faster than the optimal method. The experimental outcome indicates that CCPCFR-HPSOSA and CCPCFR-GWO outperform the other methods, consistently providing near-optimal solutions. However, CCPCFR-GWO is preferred over CCPCFR-HPSOSA due to its faster execution time. Specifically, CCPCFR-GWO achieves an average speed-up of 3.9 over the optimal for smaller networks and an average speed-up of 31.78 for larger networks, while still producing near-optimal solutions.

关键词： controller capacity metaheuristic algorithms optimal placement of controllers propagation latency software defined network

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metaheuristic algorithms and Polynomial Turing Reductions: A Case Study Based on Ant Colony Optimization

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Procedia Computer Science 2015年 46卷 388-395页

作者： Anandkumar Prakasam Nickolas Savarimuthu Department of Computer Applications National Institute of Technology Trichy

Nowadays, there is an increasing dependence on metaheuristic algorithms for solving combinatorial optimization problems. This paper discusses various metaheuristic algorithms, their similarities and differences and how Ant Colony Optimization algorithm is found to be much more suitable for providing a generic implementation. We start with the solution for Travelling Salesman Problem using Ant Colony Optimization (ACO) and show how Polynomial Turing Reduction helps us solve Job Shop Scheduling and Knapsack Problems without making considerable changes in the implementation. The probabilistic nature of metaheuristic algorithms, especially ACO helps us to a greater extent in avoiding parameter fine-tuning. Through Sensitivity analysis we find that ACO exhibits better resilience to changes in parameter values in comparison to other metaheuristic algorithms.

关键词： Ant Colony Optimization Turing Reductions metaheuristic algorithms Travelling Salesman Problem Job Shop Scheduling Knapsack Problem

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Application of metaheuristic algorithms in DC-optimal power flow

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AFRICAN JOURNAL OF SCIENCE TECHNOLOGY INNOVATION & DEVELOPMENT 2020年第7期12卷 867-872页

作者： Ajayi, Oluwafemi Nwulu, Nnamdi Damisa, Uyikumhe Univ Johannesburg Dept Elect & Elect Engn Sci Johannesburg South Africa

This paper presents the application of two metaheuristic algorithms (Exchange Market Algorithm and Ant Lion Optimizer) in solving the DC optimal power flow problem. The objective of this study is to minimize fuel costs associated with electricity generation. Advanced Interactive Multidimensional Modelling Software is also used to solve the same optimization problem and the results obtained from using this method are used to validate those from the two metaheuristic algorithms. The three methods have been implemented on the standard IEEE 14- and 30-bus system, as well as the 62-bus Indian utility system. From the analysis, the results obtained prove the robustness and effectiveness of using both algorithms to solve DC optimal power flow and even more complex optimization problems.

关键词： Ant Lion Optimizer Exchange Market Algorithm metaheuristic algorithms optimal power flow

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Improving the Performance of Analog Integrated Circuits using Multi-Objective metaheuristic algorithms 5

Improving the Performance of Analog Integrated Circuits usin...

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IEEE 5th Conference on Knowledge Based Engineering and Innovation (KBEI)

作者： Shahraki, Najmeh Sayyadi Mohammadi, Ali Mohammadi-Esfahrood, Sadegh Zahiri, Seyed Hamid Univ Birjand Dept Elect & Comp Engn Birjand Iran

ISBN: (纸本)9781728108728

Low-Noise Amplifier (LNA) is the first critical component and an important part of the analog integrated systems and wireless communication technology. LNA plays a key role in the design of Radio Frequency (RF) circuits. High voltage gain, low power consumption, high bandwidth and low Noise Figure (NF) are among the most prominent characteristics of LNAs. In this paper, in order to establish an appropriate tradeoff between circuit contradictory objectives and overcoming the design problem of an efficient LNA, the approach is focused on utilizing metaheuristic optimization methods for elements intelligent sizing and circuit automatic design. For this purpose, the Computer-Aided Design (CAD) tool based on the new and powerful version of Multi-Objective Gray Wolf Optimization (MOGWO) has been used. Implementation of algorithms in Matlab and circuit simulations in Hspice has done. Simulation results, in contrast to other research, not only meet the design specifications, but also provide a variety of solutions under the "Pareto-optimality", which allows designers to have more design options. Also, the evaluations indicate the close competition between the proposed method and other commonly used methods.

关键词： Low-noise amplifier metaheuristic algorithms Multi-objective optimization Gray wolf optimization Performance improvement

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metaheuristic algorithms Applied to Color Image Segmentation on HSV Space

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JOURNAL OF IMAGING 2022年第1期8卷 6-6页

作者： Giuliani, Donatella Univ Bologna Sch Econ Management & Stat I-40126 Bologna Italy

In this research, we propose an unsupervised method for segmentation and edge extraction of color images on the HSV space. This approach is composed of two different phases in which are applied two metaheuristic algorithms, respectively the Firefly (FA) and the Artificial Bee Colony (ABC) algorithms. In the first phase, we performed a pixel-based segmentation on each color channel, applying the FA algorithm and the Gaussian Mixture Model. The FA algorithm automatically detects the number of clusters, given by histogram maxima of each single-band image. The detected maxima define the initial means for the parameter estimation of the GMM. Applying the Bayes' rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. After processing each color channel, we recombined the segmented components in the final multichannel image. A further reduction in the resultant cluster colors is obtained using the inner product as a similarity index. In the second phase, once we have assigned all pixels to the corresponding classes of the HSV space, we carry out the second step with a region-based segmentation applied to the corresponding grayscale image. For this purpose, the bioinspired Artificial Bee Colony algorithm is performed for edge extraction.

关键词： color image segmentation color image processing metaheuristic algorithms firefly algorithm artificial bee colony algorithm

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