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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Single-nucleotide polymorphism (SNP) analysis has become a pivotal strategy for drug discovery within bioinformatics, especially for incurable diseases like cancer. With the increasing number of researchers starting to embrace metaheuristic methods, a review was done by gathering papers from the Google Scholar database from 2018 to 2023, which resulted in 20 papers after title, abstract, and content filtering. The findings show that the Genetic Algorithm and the Harmony Search Algorithm have become popular approaches in SNP analysis, particularly in studies on breast cancer, age-related macular degeneration, and colorectal cancer. However, the review shows that while researchers have proven most methods effective in finding disease-related SNPs, a more measurable study in SNP analysis is needed, due to the lack of elaboration on measurement metrics in the found studies. © 2024 IEEE.

关键词： metaheuristic algorithms single nucleotide polymorphisms

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A Review on metaheuristic algorithms Employed in WSN

A Review on Metaheuristic Algorithms Employed in WSN

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Al-Ani, Khalid W. University of Anbar Department of Studies and Planning Ramadi Iraq

ISBN: (纸本)9798350372229

In recent years, Wireless Sensor Network (WSN) is in demand over the world due to its rapid deployment in a variety of applications. The challenges in the WSN like limited battery power and communication range caught the attention of the researchers to continue improving network efficiency and extend the lifetime. metaheuristic algorithms have proven efficiency in solving WSN challenges. This paper begins with an introduction about WSN, and its structure, then discusses the optimization challenges in WSN. The paper also presents the most prominent metaheuristic algorithms, their operating mechanisms, and limitations. Finally, the paper analysis of the state of art WSN metaheuristic algorithms. The studies show that using metaheuristic algorithms to solve WSN problems gave a promising result. © 2024 IEEE.

关键词： energy efficiency lifetime metaheuristic algorithms WSN

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Optimizing Feature Selection and Oversampling Using metaheuristic algorithms for Binary Fraud Detection Classification 19th

Optimizing Feature Selection and Oversampling Using Metaheur...

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19th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Biltawi, Mariam M. Qaddoura, Raneem Faris, Hossam Al Hussein Tech Univ Amman 11831 Jordan Univ Granada Res Ctr Informat & Commun Technol CITIC UGR Granada Spain Univ Jordan King Abdullah II Sch Informat Technol Amman 11942 Jordan

ISBN: (纸本)9783031341137;9783031341113;9783031341106

Identifying fraudulent transactions and preventing unauthorized individuals from revealing credit card information are essential tasks for different financial entities. Fraud detection systems are used to apply this task by identifying the fraudulent transactions from the normal ones. Usually, the data used for fraud detection is imbalanced, containing many more instances of normal transactions than fraudulent ones. This causes diminished classification task results because it is hard to train a classifier that distinguishes between them. Another problem is caused by many features under study for the fraud detection task. This paper utilizes different metaheuristic algorithms for feature selection to solve the problem of unneeded features and uses the Synthetic Minority Oversampling TEchnique (SMOTE) to solve the imbalance problem of the data using different classification algorithms. The meta-heuristic algorithms include Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), and A MultiVerse Optimizer (MVO), whereas the classification algorithms include Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The results show that applying the oversampling technique generated better results for the G-Mean and Recall values, while the feature selection process enhanced the results of almost all the classification algorithms.

关键词： Fraud Detection SMOTE Oversampling Classification metaheuristic algorithms Optimization

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Spatio-temporal modelling of asthma-prone areas using a machine learning optimized with metaheuristic algorithms

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GEOCARTO INTERNATIONAL 2022年第25期37卷 9917-9942页

作者： Razavi-Termeh, Seyed Vahid Sadeghi-Niaraki, Abolghasem Choi, Soo-Mi KN Toosi Univ Technol Fac Geodesy & Geomat Engn Geoinformat Technol Ctr Excellence Tehran Iran Sejong Univ Dept Comp Sci & Engn Seoul South Korea

The main aim of this study was to use two metaheuristic optimization algorithms-a genetic algorithm (GA) and a teaching-learning-based optimization (TLBO) algorithm-to determine the optimal parameters of a support vector regression (SVR) model for Spatio-temporal modelling of asthma-prone areas in Tehran, Iran. First, a spatial-temporal database consisting of dependent (872 patients with asthma) and independent data (air pollution, meteorology, distance to park, and street parameters) was created. In the next step, Spatio-temporal modelling and mapping of asthma-prone areas were performed using three models: SVR, SVR-GA, and SVR-TLBO. The highest accuracy of the area under the curve (AUC) of the receiver operating characteristic (ROC) was for SVR-GA (0.806, 0.801, 0.823, and 0.811), SVR-TLBO (0.8, 0.797, 0.81, and 0.803), and SVR (0.786, 0.78, 0.796, and 0.791) models in spring, summer, autumn, and winter, respectively. Autumn, winter, spring, and summer were most accurate in modelling asthma occurrence, respectively.

关键词： Asthma spatio-temporal modelling machine learning model metaheuristic algorithms geographic information system (GIS)

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Discrete variable optimization of structures subjected to dynamic loads using equivalent static loads and metaheuristic algorithms

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OPTIMIZATION AND ENGINEERING 2022年第2期23卷 643-687页

作者： Al-Bazoon, Mustafa Arora, Jasbir S. Univ Misan Dept Civil Engn Coll Engn Amarah 62001 Maysan Iraq Univ Iowa Iowa Technol Inst Iowa City IA 52242 USA

This paper presents a new computational procedure for optimization of structures subjected to dynamic loads. The optimization problem is formulated with discrete design variables that represent the members from a table of commercially available members. Also, the requirements in the American Institute of Steel Construction (AISC) manual are formulated as constraints. This results in a nondifferentiable optimization problem. In the new procedure, the dynamic load is transformed into equivalent static loads (ESLs). Then the static response optimization problem having discrete design variables is solved using a metaheuristic optimization algorithm. Three methods to calculate the ESLs are investigated. It is found that the ESL cycles cannot converge to the final design. Therefore after a few ESL cycles, the original dynamic loads need to be used in the optimization process. Four example problems are solved to analyze the procedure. Based on this analysis, it is concluded that the new procedure is more efficient compared to a procedure that does not use the ESL cycles because it reduces the total CPU effort to obtain the final design. Also, better final designs are found. The reason is that many more designs are analyzed very efficiently with the ESL procedure.

关键词： Equivalent static loads method metaheuristic algorithms Discrete variables Structural optimization Dynamic loads

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Using metaheuristic algorithms to optimize a mixed model-based ground-motion prediction model and associated variance components

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JOURNAL OF SEISMOLOGY 2022年第3期26卷 483-498页

作者： Akhani, Mohsen Pezeshk, Shahram Univ Memphis Dept Civil Engn Memphis TN 38152 USA

In this study, two metaheuristic optimization algorithms are employed to estimate a mixed model-based ground-motion model (GMM) with several variance components. Two optimization algorithms, particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), are employed to compute regression coefficients and uncertainties of the GMM by considering a one-stage maximum likelihood estimation framework. These optimization models are applied to a complex predictive equation in a way that the results best fit a ground-motion dataset. Uncertainties and the regression coefficients of a functional form of a predictive equation are estimated and compared with other models to show the strengths and the limitations of the proposed approaches. The obtained results provide reliable solutions and demonstrate good accuracy compared to previous search algorithms based on a given dataset of ground-motions. We estimated error metrics for the predicted data, and the results show that using the proposed algorithms provides better results compared to the existing search algorithms.

关键词： metaheuristic algorithms Regression coefficients Uncertainties GMM

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A new framework for reliability-based design optimization using metaheuristic algorithms

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STRUCTURES 2022年 38卷 1210-1225页

作者： Kaveh, Ali Zaerreza, Ataollah Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran

This paper presents a new framework for reliability-based design optimization (RBDO) using metaheuristic algorithms based on decoupled methods. The sequential optimization and reliability assessment (SORA) is utilized as a decoupled method in this study. In the present framework, unlike the previous RBDO based on decoupled methods, a metaheuristic is employed on both reliability assessment and the optimization parts. Here, first a new reliability assessment method based on the metaheuristics is introduced. Then, a new termination condition for the metaheuristic inspired by the termination condition of the gradient-based method is presented. In order to investigate the efficiency of the proposed framework, an enhanced shuffled shepherd optimization algorithm (ESSOA) is used as an optimization approach. The efficiency of the proposed framework is evaluated by four well-known RBDO benchmarks investigated in the previous studies using the gradient-based method. Also, the two new RBDO problems are introduced in this study. The results show that the proposed framework can have better performance than the gradient-based method in the RBDO and can easily be utilized in a wide range of RBDO problems.

关键词： Reliability-based design optimization metaheuristic algorithms Reliability assessment method Shuffled shepherd optimization algorithm

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Concrete carbonation depth prediction model based on a gradient-boosting decision tree and different metaheuristic algorithms

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 21卷

作者： Wu, Junxi Zhao, Guoyan Wang, Meng Xu, Yihang Wang, Ning Cent South Univ Sch Resource & Safety Engn Changsha 410083 Hunan Peoples R China

Carbonation is a significant factor contributing to the instability of concrete structures. This study proposes two prediction models based on the gradient boosting decision tree (GBDT) to address the challenge of predicting concrete carbonation depth. This study integrates GBDT with two metaheuristic algorithms, particle swarm optimization algorithm (PSO) and sparrow search optimization algorithm (SSA), forming two hybrid models, PSO-GBDT and SSA-GBDT, aimed at enhancing prediction accuracy. Six influencing parameters (FA, t, w/b, B, RH, and CO2) were selected as input features to train and evaluate the hybrid models, with the concrete carbonation depth was used as the output. A database containing 883 groups of cases was established. Finally, three classic models, GBDT, ANN and SVR, were compared against the two hybrid models. To enhance model generalization, all models were subjected to five-fold cross validation. Four evaluation indicators (RMSE, R-2, MAE, and VAF) and Taylor diagrams were employed to comprehensively assess these models. The results indicated that the two hybrid models exhibited superior prediction performance in the dataset (training set: R-2, VAF>0.98, testing set: R-2, VAF>0.96). The SSA-GBDT model achieved the highest prediction performance (RMSE= 2.7008, R-2= 0.9639, MAE= 1.7691, VAF= 0.9627). Additionally, the SSA-GBDT model identified exposure time (t) as the feature with the greatest impact. This study demonstrates the applicability of the SSA-GBDT models in predicting concrete carbonation depth, offering a novel approach for improving accuracy in such predictions.

关键词： Carbonation depth Gradient boosting decision tree (GBDT) metaheuristic algorithms Prediction

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Optimal electric vehicle charging and discharging scheduling using metaheuristic algorithms: V2G approach for cost reduction and grid support

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JOURNAL OF ENERGY STORAGE 2024年 90卷

作者： Shaheen, Husam I. Rashed, Ghamgeen I. Yang, Bo Yang, Jun Changsha Univ Sch Elect Informat & Elect Engn Hunan Prov Univ Key Lab Energy Storage Power Syst Changsha 410022 Hunan Peoples R China Wuhan Univ Sch Elect Engn & Automat Hubei Engn & Technol Res Ctr AC DC Intelligent Dis Wuhan 430072 Hubei Peoples R China

The adoption of Electric Vehicles (EVs) in the transportation sector is expected to grow significantly in the coming few years. While EVs offer numerous benefits, including being environmentally friendly, energyefficient, low -noise, and can intelligently interact with smart grids through Vehicle -to -Grid (V2G) technology, their widespread adoption will increase energy demand and present challenges to grid load management. Furthermore, EV users face issues such as charging costs, charging time, access to public charging infrastructure, and more. In this article, we propose an approach utilizing metaheuristic algorithms to schedule the charging and discharging activities of EVs while parking, leveraging V2G technology with the goal of reducing the daily costs of EV users and addressing energy demand management challenges in smart grids. Four metaheuristic algorithms inspired by evolutionary and swarm concepts are applied, including Differential Evolution (DE), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO). The results obtained from the proposed approach demonstrate the feasibility of scheduling EVs charging and discharging activities to minimize EV user costs through V2G integration. This, in turn, contributes to enhancing the overall EV user experience and addressing energy demand management issues. Additionally, the results show that WOA outperformed the other algorithms in terms of convergence. This work can be further developed to create an integrated algorithm to balance the interests of both EV users and parking facility operators.

关键词： Electric vehicles (EVs) Vehicle-to-grid (V2G) metaheuristic algorithms Charging and discharging scheduling Energy demand management

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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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