Currently, decision support systems (DSSs) are essential tools that provide information and support for decision making on possible problems that, due to their level of complexity, cannot be easily solved by humans [...]
{"title":"Special Issue on Algorithms in Decision Support Systems Vol.2","authors":"Edward Rolando Núñez-Valdez","doi":"10.3390/a16110512","DOIUrl":"https://doi.org/10.3390/a16110512","url":null,"abstract":"Currently, decision support systems (DSSs) are essential tools that provide information and support for decision making on possible problems that, due to their level of complexity, cannot be easily solved by humans [...]","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"107 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Silvia Carpitella, Bruno Brentan, Antonella Certa, Joaquín Izquierdo
This paper introduces a recommendation system aimed at enhancing the sustainable process of risk management within airport operations, with a special focus on Occupational Stress Risks (OSRs). The recommendation system is implemented via a flexible Python code that offers seamless integration into various operational contexts. It leverages Fuzzy Cognitive Maps (FCMs) to conduct comprehensive risk assessments, subsequently generating prioritized recommendations for predefined risk management measures aimed at preventing and/or reducing the most critical OSRs. The system’s reliability has been validated by iterating the procedure with diverse input data (i.e., matrices of varying sizes) and measures. This confirms the system’s effectiveness across a broad spectrum of engineering scenarios.
{"title":"A Recommendation System Supporting the Implementation of Sustainable Risk Management Measures in Airport Operations","authors":"Silvia Carpitella, Bruno Brentan, Antonella Certa, Joaquín Izquierdo","doi":"10.3390/a16110511","DOIUrl":"https://doi.org/10.3390/a16110511","url":null,"abstract":"This paper introduces a recommendation system aimed at enhancing the sustainable process of risk management within airport operations, with a special focus on Occupational Stress Risks (OSRs). The recommendation system is implemented via a flexible Python code that offers seamless integration into various operational contexts. It leverages Fuzzy Cognitive Maps (FCMs) to conduct comprehensive risk assessments, subsequently generating prioritized recommendations for predefined risk management measures aimed at preventing and/or reducing the most critical OSRs. The system’s reliability has been validated by iterating the procedure with diverse input data (i.e., matrices of varying sizes) and measures. This confirms the system’s effectiveness across a broad spectrum of engineering scenarios.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"45 31","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135432506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laurent Risser, Agustin Martin Picard, Lucas Hervier, Jean-Michel Loubes
The problem of algorithmic bias in machine learning has recently gained a lot of attention due to its potentially strong impact on our societies. In much the same manner, algorithmic biases can alter industrial and safety-critical machine learning applications, where high-dimensional inputs are used. This issue has, however, been mostly left out of the spotlight in the machine learning literature. Contrary to societal applications, where a set of potentially sensitive variables, such as gender or race, can be defined by common sense or by regulations to draw attention to potential risks, the sensitive variables are often unsuspected in industrial and safety-critical applications. In addition, these unsuspected sensitive variables may be indirectly represented as a latent feature of the input data. For instance, the predictions of an image classifier may be altered by reconstruction artefacts in a small subset of the training images. This raises serious and well-founded concerns about the commercial deployment of AI-based solutions, especially in a context where new regulations address bias issues in AI. The purpose of our paper is, then, to first give a large overview of recent advances in robust machine learning. Then, we propose a new procedure to detect and to treat such unknown biases. As far as we know, no equivalent procedure has been proposed in the literature so far. The procedure is also generic enough to be used in a wide variety of industrial contexts. Its relevance is demonstrated on a set of satellite images used to train a classifier. In this illustration, our technique detects that a subset of the training images has reconstruction faults, leading to systematic prediction errors that would have been unsuspected using conventional cross-validation techniques.
{"title":"Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning","authors":"Laurent Risser, Agustin Martin Picard, Lucas Hervier, Jean-Michel Loubes","doi":"10.3390/a16110510","DOIUrl":"https://doi.org/10.3390/a16110510","url":null,"abstract":"The problem of algorithmic bias in machine learning has recently gained a lot of attention due to its potentially strong impact on our societies. In much the same manner, algorithmic biases can alter industrial and safety-critical machine learning applications, where high-dimensional inputs are used. This issue has, however, been mostly left out of the spotlight in the machine learning literature. Contrary to societal applications, where a set of potentially sensitive variables, such as gender or race, can be defined by common sense or by regulations to draw attention to potential risks, the sensitive variables are often unsuspected in industrial and safety-critical applications. In addition, these unsuspected sensitive variables may be indirectly represented as a latent feature of the input data. For instance, the predictions of an image classifier may be altered by reconstruction artefacts in a small subset of the training images. This raises serious and well-founded concerns about the commercial deployment of AI-based solutions, especially in a context where new regulations address bias issues in AI. The purpose of our paper is, then, to first give a large overview of recent advances in robust machine learning. Then, we propose a new procedure to detect and to treat such unknown biases. As far as we know, no equivalent procedure has been proposed in the literature so far. The procedure is also generic enough to be used in a wide variety of industrial contexts. Its relevance is demonstrated on a set of satellite images used to train a classifier. In this illustration, our technique detects that a subset of the training images has reconstruction faults, leading to systematic prediction errors that would have been unsuspected using conventional cross-validation techniques.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"45 38","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135432502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to predict the values of the price gap between the DAM and RTM using statistical machine learning algorithms and deep neural networks. In this paper, we seek to answer these questions: What will be the impact of predicting the DAM and RTM price gap directly on the prediction performance of learning methods? How can exogenous weather data affect the price gap prediction? In this paper, several exogenous features are collected, and the impacts of these features are examined to capture the best relations between the features and the target variable. An ensemble learning algorithm, namely the Random Forest (RF), is used to select the most important features. A Long Short-Term Memory (LSTM) network is used to capture long-term dependencies in predicting direct gap values between the markets stated. Moreover, the advantages of directly predicting the gap price rather than subtracting the price predictions of the DAM and RTM are shown. The presented results are based on the California Independent System Operator (CAISO)’s electricity market data for two years. The results show that direct gap prediction using exogenous weather features decreases the error of learning methods by 46%. Therefore, the presented method mitigates the prediction error of the price gap between the DAM and RTM. Thus, the convergence bidders can increase their profit, and the ISOs can tune their mechanism accordingly.
{"title":"Predicting the Gap in the Day-Ahead and Real-Time Market Prices Leveraging Exogenous Weather Data","authors":"Nika Nizharadze, Arash Farokhi Soofi, Saeed Manshadi","doi":"10.3390/a16110508","DOIUrl":"https://doi.org/10.3390/a16110508","url":null,"abstract":"Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to predict the values of the price gap between the DAM and RTM using statistical machine learning algorithms and deep neural networks. In this paper, we seek to answer these questions: What will be the impact of predicting the DAM and RTM price gap directly on the prediction performance of learning methods? How can exogenous weather data affect the price gap prediction? In this paper, several exogenous features are collected, and the impacts of these features are examined to capture the best relations between the features and the target variable. An ensemble learning algorithm, namely the Random Forest (RF), is used to select the most important features. A Long Short-Term Memory (LSTM) network is used to capture long-term dependencies in predicting direct gap values between the markets stated. Moreover, the advantages of directly predicting the gap price rather than subtracting the price predictions of the DAM and RTM are shown. The presented results are based on the California Independent System Operator (CAISO)’s electricity market data for two years. The results show that direct gap prediction using exogenous weather features decreases the error of learning methods by 46%. Therefore, the presented method mitigates the prediction error of the price gap between the DAM and RTM. Thus, the convergence bidders can increase their profit, and the ISOs can tune their mechanism accordingly.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"32 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135773349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sai Bharadwaj Appakaya, Ruchira Pratihar, Ravi Sankar
Parkinson’s disease (PD) classification through speech has been an advancing field of research because of its ease of acquisition and processing. The minimal infrastructure requirements of the system have also made it suitable for telemonitoring applications. Researchers have studied the effects of PD on speech from various perspectives using different speech tasks. Typical speech deficits due to PD include voice monotony (e.g., monopitch), breathy or rough quality, and articulatory errors. In connected speech, these symptoms are more emphatic, which is also the basis for speech assessment in popular rating scales used for PD, like the Unified Parkinson’s Disease Rating Scale (UPDRS) and Hoehn and Yahr (HY). The current study introduces an innovative framework that integrates pitch-synchronous segmentation and an optimized set of features to investigate and analyze continuous speech from both PD patients and healthy controls (HC). Comparison of the proposed framework against existing methods has shown its superiority in classification performance and mitigation of overfitting in machine learning models. A set of optimal classifiers with unbiased decision-making was identified after comparing several machine learning models. The outcomes yielded by the classifiers demonstrate that the framework effectively learns the intrinsic characteristics of PD from connected speech, which can potentially offer valuable assistance in clinical diagnosis.
通过言语分类帕金森病(PD)因其易于获取和处理而成为一个前沿研究领域。该系统对基础设施的最低要求也使其适合远程监控应用。研究者利用不同的言语任务从不同的角度研究了PD对言语的影响。由PD引起的典型言语缺陷包括声音单调(例如，单音)，呼吸或粗糙的质量，以及发音错误。在关联言语中，这些症状更加突出，这也是常用的PD评定量表(如统一帕金森病评定量表(UPDRS)和Hoehn and Yahr (HY))的言语评估基础。目前的研究引入了一个创新的框架，该框架集成了音高同步分割和一组优化的功能，用于调查和分析PD患者和健康对照(HC)的连续语音。将提出的框架与现有方法进行比较，表明其在分类性能和缓解机器学习模型的过拟合方面具有优势。通过比较几种机器学习模型，确定了一组具有无偏决策的最优分类器。分类器产生的结果表明，该框架可以有效地从连接语音中学习PD的内在特征，这可能为临床诊断提供有价值的帮助。
{"title":"Parkinson’s Disease Classification Framework Using Vocal Dynamics in Connected Speech","authors":"Sai Bharadwaj Appakaya, Ruchira Pratihar, Ravi Sankar","doi":"10.3390/a16110509","DOIUrl":"https://doi.org/10.3390/a16110509","url":null,"abstract":"Parkinson’s disease (PD) classification through speech has been an advancing field of research because of its ease of acquisition and processing. The minimal infrastructure requirements of the system have also made it suitable for telemonitoring applications. Researchers have studied the effects of PD on speech from various perspectives using different speech tasks. Typical speech deficits due to PD include voice monotony (e.g., monopitch), breathy or rough quality, and articulatory errors. In connected speech, these symptoms are more emphatic, which is also the basis for speech assessment in popular rating scales used for PD, like the Unified Parkinson’s Disease Rating Scale (UPDRS) and Hoehn and Yahr (HY). The current study introduces an innovative framework that integrates pitch-synchronous segmentation and an optimized set of features to investigate and analyze continuous speech from both PD patients and healthy controls (HC). Comparison of the proposed framework against existing methods has shown its superiority in classification performance and mitigation of overfitting in machine learning models. A set of optimal classifiers with unbiased decision-making was identified after comparing several machine learning models. The outcomes yielded by the classifiers demonstrate that the framework effectively learns the intrinsic characteristics of PD from connected speech, which can potentially offer valuable assistance in clinical diagnosis.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"30 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135774659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known “Fake News” dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial naïve bayes (MNB), gaussian naïve bayes (GNB), Bernoulli naïve Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem.
{"title":"Deep Dive into Fake News Detection: Feature-Centric Classification with Ensemble and Deep Learning Methods","authors":"Fawaz Khaled Alarfaj, Jawad Abbas Khan","doi":"10.3390/a16110507","DOIUrl":"https://doi.org/10.3390/a16110507","url":null,"abstract":"The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known “Fake News” dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial naïve bayes (MNB), gaussian naïve bayes (GNB), Bernoulli naïve Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"39 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135868337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01Epub Date: 2023-08-29DOI: 10.3114/fuse.2023.12.08
N Mao, Y Y Xu, Y X Zhang, H Zhou, X B Huang, C L Hou, L Fan
Helvella is a widespread, frequently encountered fungal group appearing in forests, but the species diversity and molecular phylogeny of Helvella in China remains incompletely understood. In this work, we performed comprehensive phylogenetic analyses using multilocus sequence data. Six datasets were employed, including a five-locus concatenated dataset (ITS, nrLSU, tef1-α, rpb2, hsp), a two-locus concatenated dataset (ITS, nrLSU), and four single-locus datasets (ITS) that were divided based on the four different phylogenetic clades of Helvella recognized in this study. A total of I 946 sequences were used, of which 713 were newly generated, including 170 sequences of ITS, 174 sequences of nrLSU, 131 sequences of tef1-α, 107 sequences of rpb2 and 131 sequences of hsp. The phylogeny based on the five-locus concatenated dataset revealed that Helvellas. str. is monophyletic and four phylogenetic clades are clearly recognized, i.e., Acetabulum clade, Crispa clade, Elastica clade, and Lacunosa clade. A total of 24 lineages or subclades were recognized, II of which were new, the remaining 13 corresponding with previous studies. Chinese Helvella species are distributed in 22 lineages across four clades. Phylogenetic analyses based on the two-locus concatenated dataset and four single-locus datasets confirmed the presence of at least 93 phylogenetic species in China. Among them, 58 are identified as known species, including a species with a newly designated lectotype and epitype, 18 are newly described in this paper, and the remaining 17 taxa are putatively new to science but remain unnamed due to the paucity or absence of ascomatal materials. In addition, the Helvella species previously recorded in China are discussed. A list of 76 confirmed species, including newly proposed species, is provided. The occurrence of H. crispa and H. elastica are not confirmed although both are commonly recorded in China. Citation: Mao N, Xu YY, Zhang YX, Zhou H, Huang XB, Hou CL, Fan L (2023). Phylogeny and species diversity of the genus Helvella with emphasis on eighteen new species from China. Fungal Systematics and Evolution12: 111-152. doi: 10.3114/fuse.2023.12.08.
{"title":"Phylogeny and species diversity of the genus <i>Helvella</i> with emphasis on eighteen new species from China.","authors":"N Mao, Y Y Xu, Y X Zhang, H Zhou, X B Huang, C L Hou, L Fan","doi":"10.3114/fuse.2023.12.08","DOIUrl":"10.3114/fuse.2023.12.08","url":null,"abstract":"<p><p><b></b> <i>Helvella</i> is a widespread, frequently encountered fungal group appearing in forests, but the species diversity and molecular phylogeny of <i>Helvella</i> in China remains incompletely understood. In this work, we performed comprehensive phylogenetic analyses using multilocus sequence data. Six datasets were employed, including a five-locus concatenated dataset (ITS, nrLSU, <i>tef1-α, rpb2, hsp)</i>, a two-locus concatenated dataset (ITS, nrLSU), and four single-locus datasets (ITS) that were divided based on the four different phylogenetic clades of <i>Helvella</i> recognized in this study. A total of I 946 sequences were used, of which 713 were newly generated, including 170 sequences of ITS, 174 sequences of nrLSU, 131 sequences of <i>tef1-α</i>, 107 sequences of <i>rpb2</i> and 131 sequences of <i>hsp.</i> The phylogeny based on the five-locus concatenated dataset revealed that <i>Helvellas. str</i>. is monophyletic and four phylogenetic clades are clearly recognized, <i>i.e., Acetabulum</i> clade, <i>Crispa</i> clade, <i>Elastica</i> clade, and <i>Lacunosa</i> clade. A total of 24 lineages or subclades were recognized, II of which were new, the remaining 13 corresponding with previous studies. Chinese <i>Helvella</i> species are distributed in 22 lineages across four clades. Phylogenetic analyses based on the two-locus concatenated dataset and four single-locus datasets confirmed the presence of at least 93 phylogenetic species in China. Among them, 58 are identified as known species, including a species with a newly designated lectotype and epitype, 18 are newly described in this paper, and the remaining 17 taxa are putatively new to science but remain unnamed due to the paucity or absence of ascomatal materials. In addition, the <i>Helvella</i> species previously recorded in China are discussed. A list of 76 confirmed species, including newly proposed species, is provided. The occurrence of <i>H. crispa</i> and <i>H. elastica</i> are not confirmed although both are commonly recorded in China. <b>Citation:</b> Mao N, Xu YY, Zhang YX, Zhou H, Huang XB, Hou CL, Fan L (2023). Phylogeny and species diversity of the genus <i>Helvella</i> with emphasis on eighteen new species from China. <i>Fungal Systematics and Evolution</i> <b>12</b>: 111-152. doi: 10.3114/fuse.2023.12.08.</p>","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"10 1","pages":"111-152"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10964050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78899145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vladimir Korkhov, Ivan Gankevich, Anton Gavrikov, Maria Mingazova, Ivan Petriakov, Dmitrii Tereshchenko, Artem Shatalin, Vitaly Slobodskoy
Bottlenecks and imbalance in parallel programs can significantly affect performance of parallel execution. Finding these bottlenecks is a key issue in performance analysis of MPI programs especially on a large scale. One of the ways to discover bottlenecks is to analyze the critical path of the parallel program: the longest execution path in the program activity graph. There are a number of methods of finding the critical path; however, most of them suffer a performance drop when scaled. In this paper, we analyze several methods of critical path finding based on classical Dijkstra and Delta-stepping algorithms along with the proposed algorithm based on topological sorting. Corresponding algorithms for each approach are presented including additional enhancements for increasing performance. The implementation of the algorithms and resulting performance for several benchmark applications (NAS Parallel Benchmarks, CP2K, OpenFOAM, LAMMPS, and MiniFE) are analyzed and discussed.
{"title":"Finding Bottlenecks in Message Passing Interface Programs by Scalable Critical Path Analysis","authors":"Vladimir Korkhov, Ivan Gankevich, Anton Gavrikov, Maria Mingazova, Ivan Petriakov, Dmitrii Tereshchenko, Artem Shatalin, Vitaly Slobodskoy","doi":"10.3390/a16110505","DOIUrl":"https://doi.org/10.3390/a16110505","url":null,"abstract":"Bottlenecks and imbalance in parallel programs can significantly affect performance of parallel execution. Finding these bottlenecks is a key issue in performance analysis of MPI programs especially on a large scale. One of the ways to discover bottlenecks is to analyze the critical path of the parallel program: the longest execution path in the program activity graph. There are a number of methods of finding the critical path; however, most of them suffer a performance drop when scaled. In this paper, we analyze several methods of critical path finding based on classical Dijkstra and Delta-stepping algorithms along with the proposed algorithm based on topological sorting. Corresponding algorithms for each approach are presented including additional enhancements for increasing performance. The implementation of the algorithms and resulting performance for several benchmark applications (NAS Parallel Benchmarks, CP2K, OpenFOAM, LAMMPS, and MiniFE) are analyzed and discussed.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"181 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135869745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations.
{"title":"Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line","authors":"Amirreza Nickkar, Young-Jae Lee","doi":"10.3390/a16110506","DOIUrl":"https://doi.org/10.3390/a16110506","url":null,"abstract":"Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"2007 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human decision-makers (DMs)’ preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the Pareto-optimal Front (POF) may result in more preferred or suitable solutions. This study proposes approaches that enable DMs to influence the search process with their preferences by reformulating the optimization problems as constrained problems. The subsequent constrained problems are solved using various constraint handling approaches, such as the penalization of infeasible solutions and the restriction of the search to the feasible region of the search space. The proposed constraint handling approaches are compared by incorporating the approaches into a differential evolution (DE) algorithm and measuring the algorithm’s performance using both standard performance measures for dynamic multi-objective optimization (DMOO), as well as newly proposed measures for constrained DMOPs. The new measures indicate how well an algorithm was able to find solutions in the objective space that best reflect the DM’s preferences and the Pareto-optimality goal of dynamic multi-objective optimization algorithms (DMOAs). The results indicate that the constraint handling approaches are effective in finding Pareto-optimal solutions that satisfy the preference constraints of a DM.
{"title":"Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization","authors":"Adekunle Rotimi Adekoya, Mardé Helbig","doi":"10.3390/a16110504","DOIUrl":"https://doi.org/10.3390/a16110504","url":null,"abstract":"Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human decision-makers (DMs)’ preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the Pareto-optimal Front (POF) may result in more preferred or suitable solutions. This study proposes approaches that enable DMs to influence the search process with their preferences by reformulating the optimization problems as constrained problems. The subsequent constrained problems are solved using various constraint handling approaches, such as the penalization of infeasible solutions and the restriction of the search to the feasible region of the search space. The proposed constraint handling approaches are compared by incorporating the approaches into a differential evolution (DE) algorithm and measuring the algorithm’s performance using both standard performance measures for dynamic multi-objective optimization (DMOO), as well as newly proposed measures for constrained DMOPs. The new measures indicate how well an algorithm was able to find solutions in the objective space that best reflect the DM’s preferences and the Pareto-optimality goal of dynamic multi-objective optimization algorithms (DMOAs). The results indicate that the constraint handling approaches are effective in finding Pareto-optimal solutions that satisfy the preference constraints of a DM.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"2023 7-8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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