Drones are applied for normal subjects likewise as forces exercise. Consultations with drones are liable to deal with and compromise since they're broadly used for self-directed conduct. Still, it is of great consequence to take on an automatic pilot framework which is vigorous to potential digital assault. Right now, we tend to propose an individual's implicit intent recognition model dependent on a multi-modular data that is a blend of the eye movement data and the EEG signal acquired from some eye-locators and EEG scanners separately. The eye movement will be used to extricate some highlights like fixation length and fixation count relating to the visual stimuli, and similarly, we will examine the graph signals observed on part synchronicity technique and consolidating this, we will also train a few classifiers such as the SVM classifier, Naïve Bayesian and Gaussian Mixture Model that might effectively recognize an individual's implicit intention into 2 characterized classes - navigational and informational intention, which will ultimately be used for training a drone. Also, we will be displaying a biometric framework to scramble letters between a drone and an electronic ground station which can be achieved by creating a key from the EEG signal of a user. Then, at the endpoint, once the correspondence with a drone is assaulted a security system facilitates it to a sheltered ‘home’ area.
{"title":"Operating of a Drone Using Human Intent Recognition and Characteristics of an EEG Signal","authors":"Ashutosh Shankhdhar, Arushi Mangla, Akhilesh Kumar Singh, Ayushi Srivastava","doi":"10.1109/PDGC50313.2020.9315321","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315321","url":null,"abstract":"Drones are applied for normal subjects likewise as forces exercise. Consultations with drones are liable to deal with and compromise since they're broadly used for self-directed conduct. Still, it is of great consequence to take on an automatic pilot framework which is vigorous to potential digital assault. Right now, we tend to propose an individual's implicit intent recognition model dependent on a multi-modular data that is a blend of the eye movement data and the EEG signal acquired from some eye-locators and EEG scanners separately. The eye movement will be used to extricate some highlights like fixation length and fixation count relating to the visual stimuli, and similarly, we will examine the graph signals observed on part synchronicity technique and consolidating this, we will also train a few classifiers such as the SVM classifier, Naïve Bayesian and Gaussian Mixture Model that might effectively recognize an individual's implicit intention into 2 characterized classes - navigational and informational intention, which will ultimately be used for training a drone. Also, we will be displaying a biometric framework to scramble letters between a drone and an electronic ground station which can be achieved by creating a key from the EEG signal of a user. Then, at the endpoint, once the correspondence with a drone is assaulted a security system facilitates it to a sheltered ‘home’ area.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127109136","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315323
Ajit Singh, P. Chakraborty
Often a tool collecting traces for lock-intensive applications adds overheads of its own and distorts the lock-related measurements. We highlight why tool-overhead is particularly problematic for lock-intensive applications. Tool-overhead has received limited attention in existing research. The primary reason for high tool-overhead, as per our analysis is cache- coherence related overheads for tracing tool data structure. Using the insight, we develop Mutexis, an optimized user-level dynamic binary instrumentation (DBI) tracing PIN tool. To show tool effectiveness, we use lock-intensive applications from PARSEC and Splash3X benchmarks. We compare the proposed tool's overhead with tool-overhead of other researchers. The tool-overhead of mutexis is minimal, growing up to 2.1X for lock- intensive applications (4X to lOOX for others) and is negligible in most cases. This is so, even when our tool captures detail cycle- stamped traces of POSIX lock function compared to limited aggregate statistics collected by other researchers tools.
{"title":"Optimizing Trace Tool-overhead for Lock-Intensive Multi-threaded Parallel Applications","authors":"Ajit Singh, P. Chakraborty","doi":"10.1109/PDGC50313.2020.9315323","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315323","url":null,"abstract":"Often a tool collecting traces for lock-intensive applications adds overheads of its own and distorts the lock-related measurements. We highlight why tool-overhead is particularly problematic for lock-intensive applications. Tool-overhead has received limited attention in existing research. The primary reason for high tool-overhead, as per our analysis is cache- coherence related overheads for tracing tool data structure. Using the insight, we develop Mutexis, an optimized user-level dynamic binary instrumentation (DBI) tracing PIN tool. To show tool effectiveness, we use lock-intensive applications from PARSEC and Splash3X benchmarks. We compare the proposed tool's overhead with tool-overhead of other researchers. The tool-overhead of mutexis is minimal, growing up to 2.1X for lock- intensive applications (4X to lOOX for others) and is negligible in most cases. This is so, even when our tool captures detail cycle- stamped traces of POSIX lock function compared to limited aggregate statistics collected by other researchers tools.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"57 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123706322","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315856
Aditya Mohan, Kushagra Srivastava, Garima Malhotra, N. U. Khan
Farmer suicides in India had ranged between 1.4 and 1.8 per hundred thousand people, accounting to 11.2 % of all suicides in India due to reasons like debt, low produce prices, crops failure and alcohol addiction. Among these, crop failure is attributed to various factors including unpredictable weather conditions, poor farming practices, pests and diseases along withill use of fertilizers and late disease diagnosis. Various systems have been proposed and implemented for immediate identification of the disease, using mobile devices for disease identification and consequent action, but the majority of proposed approaches involve segmentation techniques coupled with classical machine learning algorithms, which focused on the entire plant or fruit image, not primarily on the diseased part, thus embedding pixels which introduce possible bias in each data point leading to an imprecise training dataset and consequently faulty training. In this paper we propose a method of leveraging a combination of clustering based segmentation for identification of the diseased part exclusively and consequent feature extraction over it along with using neural networks over classical algorithms, thereby increasing feature complexity and thus better training, increasing training accuracy and leaving scope for further integration of huge amount of data which can added later on.
{"title":"Plant Disease Detection Using Clustering Based Segmentation and Neural Networks","authors":"Aditya Mohan, Kushagra Srivastava, Garima Malhotra, N. U. Khan","doi":"10.1109/PDGC50313.2020.9315856","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315856","url":null,"abstract":"Farmer suicides in India had ranged between 1.4 and 1.8 per hundred thousand people, accounting to 11.2 % of all suicides in India due to reasons like debt, low produce prices, crops failure and alcohol addiction. Among these, crop failure is attributed to various factors including unpredictable weather conditions, poor farming practices, pests and diseases along withill use of fertilizers and late disease diagnosis. Various systems have been proposed and implemented for immediate identification of the disease, using mobile devices for disease identification and consequent action, but the majority of proposed approaches involve segmentation techniques coupled with classical machine learning algorithms, which focused on the entire plant or fruit image, not primarily on the diseased part, thus embedding pixels which introduce possible bias in each data point leading to an imprecise training dataset and consequently faulty training. In this paper we propose a method of leveraging a combination of clustering based segmentation for identification of the diseased part exclusively and consequent feature extraction over it along with using neural networks over classical algorithms, thereby increasing feature complexity and thus better training, increasing training accuracy and leaving scope for further integration of huge amount of data which can added later on.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116089236","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315776
Shally, S. Sharma, Sunil Kumar
Ubiquitous access has made the cloud computing very popular. Due to its wide acceptability, size of cloud data center is increasing every day. The enormous size of the datacenter is posing a challenge to the cloud service providers in terms of huge electricity bills. It has its environmental impact too in terms of carbon footprint. Hence, managing the cloud resources in energy efficient way is the need of the hour. Researchers have proposed many energy efficient methods based upon the concept of switching off idle machines. There is very less focus on equally important network component i.e. switch. In the same direction we have proposed a Switch based Power Aware (SPA) VM consolidation method to minimize the energy consumption of the cloud data center by considering the utilization rate of physical machines as well as the switches of the cloud datacenter. The result of the proposed method shows a significant decrease in the energy consumption of the cloud datacenter.
{"title":"A Switch Based Power Aware VM Consolidation Method for Cloud Datacenter","authors":"Shally, S. Sharma, Sunil Kumar","doi":"10.1109/PDGC50313.2020.9315776","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315776","url":null,"abstract":"Ubiquitous access has made the cloud computing very popular. Due to its wide acceptability, size of cloud data center is increasing every day. The enormous size of the datacenter is posing a challenge to the cloud service providers in terms of huge electricity bills. It has its environmental impact too in terms of carbon footprint. Hence, managing the cloud resources in energy efficient way is the need of the hour. Researchers have proposed many energy efficient methods based upon the concept of switching off idle machines. There is very less focus on equally important network component i.e. switch. In the same direction we have proposed a Switch based Power Aware (SPA) VM consolidation method to minimize the energy consumption of the cloud data center by considering the utilization rate of physical machines as well as the switches of the cloud datacenter. The result of the proposed method shows a significant decrease in the energy consumption of the cloud datacenter.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116103159","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 : 2020-11-06DOI: 10.1007/s11042-021-11488-6
Somya Goyal, P. Bhatia
{"title":"Heterogeneous Stacked Ensemble Classifier for Software Defect Prediction","authors":"Somya Goyal, P. Bhatia","doi":"10.1007/s11042-021-11488-6","DOIUrl":"https://doi.org/10.1007/s11042-021-11488-6","url":null,"abstract":"","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126916201","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315779
Vidhi Sood, D. Kumar, V. Athavale, S. Gupta
In medical Science field, the EEG signal classification is present with a plethora of applications. The health monitoring is depending on modern technology like EEG and ECG signal recording. The EEG signals are analyzed to identify the abnormal condition of the human brains. The unusual activity of the brain is known as the seizure The electrical signal generated in the braincauses epilepsy. In this proposed work, a deep learning model Bi-LSTM is projected for the epilepsy signal classification. The Bonn university EEG dataset is used for the testing purpose. The EEG signal classification has three significant steps data pre-processing, features extraction, and classification. Data pre-processing is done by DCT and filter conversion. The Hurst exponent and ARMA feature sets are extracted from the pre-process EEG signal. A Bayesian optimization tuned Bi-LSTM model is suggested for the EEG signal classification task. The epileptic EEG signals are recognized by the proposed method. The hyperparameters of the Bi- LSTM model is tuned by the Bayesian optimization rule. Three different class ictal, pre-ictal, and inter-ictal are classified from the EEG signal data. A comparative study is also provided for the epilepsy signal classification task. The classification accuracy of for ictal is 94%, pre-ictal is 92%, and inter-ictal is 91%, which more significant than the LSTM and SVM based classifier model.
{"title":"Epilepsy Seizure Detection by using Bayesian Optimize Bi-LSTM Model","authors":"Vidhi Sood, D. Kumar, V. Athavale, S. Gupta","doi":"10.1109/PDGC50313.2020.9315779","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315779","url":null,"abstract":"In medical Science field, the EEG signal classification is present with a plethora of applications. The health monitoring is depending on modern technology like EEG and ECG signal recording. The EEG signals are analyzed to identify the abnormal condition of the human brains. The unusual activity of the brain is known as the seizure The electrical signal generated in the braincauses epilepsy. In this proposed work, a deep learning model Bi-LSTM is projected for the epilepsy signal classification. The Bonn university EEG dataset is used for the testing purpose. The EEG signal classification has three significant steps data pre-processing, features extraction, and classification. Data pre-processing is done by DCT and filter conversion. The Hurst exponent and ARMA feature sets are extracted from the pre-process EEG signal. A Bayesian optimization tuned Bi-LSTM model is suggested for the EEG signal classification task. The epileptic EEG signals are recognized by the proposed method. The hyperparameters of the Bi- LSTM model is tuned by the Bayesian optimization rule. Three different class ictal, pre-ictal, and inter-ictal are classified from the EEG signal data. A comparative study is also provided for the epilepsy signal classification task. The classification accuracy of for ictal is 94%, pre-ictal is 92%, and inter-ictal is 91%, which more significant than the LSTM and SVM based classifier model.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130675095","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315797
Mohamed Naseer, Zayed Khaled, Islam Tharwat Abdel Halim, Ayman M. Bahaa-Eldin
Even though the architecture and programming models of heterogeneous many-core processors significantly differ from the conventional multi-core processors, their overall performance is promising for future computing systems. The application programs should be suitably parallel to unlock such potential and match the underlying heterogeneous platform. Therefore, high-level programming constructs should be provided by parallel programming models for heterogeneous many-cores to avoid recurrent programming errors while communicating in heterogeneous many-core systems. Synchronization is one of the key problems in building shared-resource-based parallel software. In this article, we survey mainstream and novel parallel programming models that handle this troublesome issue for heterogeneous Many-Cores: OpenMP, CUDA, OpenCL, Go, Kokkos, OmpSs, and XcalableMP. We also discuss potential research directions in the area.
{"title":"Synchronization in Parallel Programming Models for Heterogeneous Many-Cores","authors":"Mohamed Naseer, Zayed Khaled, Islam Tharwat Abdel Halim, Ayman M. Bahaa-Eldin","doi":"10.1109/PDGC50313.2020.9315797","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315797","url":null,"abstract":"Even though the architecture and programming models of heterogeneous many-core processors significantly differ from the conventional multi-core processors, their overall performance is promising for future computing systems. The application programs should be suitably parallel to unlock such potential and match the underlying heterogeneous platform. Therefore, high-level programming constructs should be provided by parallel programming models for heterogeneous many-cores to avoid recurrent programming errors while communicating in heterogeneous many-core systems. Synchronization is one of the key problems in building shared-resource-based parallel software. In this article, we survey mainstream and novel parallel programming models that handle this troublesome issue for heterogeneous Many-Cores: OpenMP, CUDA, OpenCL, Go, Kokkos, OmpSs, and XcalableMP. We also discuss potential research directions in the area.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"571 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123144006","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 : 2020-11-06DOI: 10.1109/pdgc50313.2020.9315830
{"title":"[Copyright notice]","authors":"","doi":"10.1109/pdgc50313.2020.9315830","DOIUrl":"https://doi.org/10.1109/pdgc50313.2020.9315830","url":null,"abstract":"","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124353400","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315814
Sangeeta Vaibhav Meena, V. Dhaka, Deepak Sinwar
The economy of the agriculture industry is badly affected by plant diseases. Effective management practices involve regular monitoring of the plant's health with early detection of pathogens for reducing the spread of diseases. Traditionally, several invasive plant disease diagnostic techniques are used that involve the devastation of leaf samples with chemical treatment. Apart from that, non-invasive disease detection techniques are more feasible and practical ways of monitoring plant diseases in real time applications without affecting the growth of plants. Imaging and spectroscopic are non-invasive disease identification methods used for discovering harmful organisms that affect the health of plants. For identifying diseases, biophysical parameters of plants are extracted through vegetation indices. A vegetation index is a spectral computation that can be done using two or more spectral bands that are sensitive to plant vigor and biomass. Vegetation indices are used to estimate water contents of soils, monitor drought, classify vegetation, examine climate trends, crop management, identify changes in biodiversity, etc. The paper aims to discuss various methods used for detecting plant diseases. Some commonly used vegetation indices are also discussed along with the role of vegetation indices in identifying plant diseases.
{"title":"Exploring the Role of Vegetation Indices in Plant Diseases Identification","authors":"Sangeeta Vaibhav Meena, V. Dhaka, Deepak Sinwar","doi":"10.1109/PDGC50313.2020.9315814","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315814","url":null,"abstract":"The economy of the agriculture industry is badly affected by plant diseases. Effective management practices involve regular monitoring of the plant's health with early detection of pathogens for reducing the spread of diseases. Traditionally, several invasive plant disease diagnostic techniques are used that involve the devastation of leaf samples with chemical treatment. Apart from that, non-invasive disease detection techniques are more feasible and practical ways of monitoring plant diseases in real time applications without affecting the growth of plants. Imaging and spectroscopic are non-invasive disease identification methods used for discovering harmful organisms that affect the health of plants. For identifying diseases, biophysical parameters of plants are extracted through vegetation indices. A vegetation index is a spectral computation that can be done using two or more spectral bands that are sensitive to plant vigor and biomass. Vegetation indices are used to estimate water contents of soils, monitor drought, classify vegetation, examine climate trends, crop management, identify changes in biodiversity, etc. The paper aims to discuss various methods used for detecting plant diseases. Some commonly used vegetation indices are also discussed along with the role of vegetation indices in identifying plant diseases.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134172852","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 : 2020-11-06DOI: 10.1109/PDGC50313.2020.9315798
Mohammad Sajid, A. Jafar, Surbhi Sharma
The vehicle routing problem is a well-known combinatorial optimization problem and its optimization has impact on various domains including smart logistics, smart cities, unmanned air vehicle routing and others. In Capacitated Vehicle Routing Problem (CVRP), the known demands of customers are fulfilled by identical vehicles with objective to optimize the cost in terms of distance. In this work, we propose to solve CVRP using Hybrid Genetic and Simulated Annealing (HGSA) Algorithm to optimize the total travelled distance. The proposed HGSA algorithm combines genetic algorithm and simulated annealing to search global optimal solutions. The HGSA algorithm employs novel nearest-neighbor crossover operator which generates solutions based on nearest-neighbors so that the total travelled distance remains minimum possible. The proposed HGSA Algorithm was tested with 86 benchmark CVRP instances and the effectiveness of HGSA is shown by the results offered.
{"title":"Hybrid Genetic and Simulated Annealing Algorithm for Capacitated Vehicle Routing Problem","authors":"Mohammad Sajid, A. Jafar, Surbhi Sharma","doi":"10.1109/PDGC50313.2020.9315798","DOIUrl":"https://doi.org/10.1109/PDGC50313.2020.9315798","url":null,"abstract":"The vehicle routing problem is a well-known combinatorial optimization problem and its optimization has impact on various domains including smart logistics, smart cities, unmanned air vehicle routing and others. In Capacitated Vehicle Routing Problem (CVRP), the known demands of customers are fulfilled by identical vehicles with objective to optimize the cost in terms of distance. In this work, we propose to solve CVRP using Hybrid Genetic and Simulated Annealing (HGSA) Algorithm to optimize the total travelled distance. The proposed HGSA algorithm combines genetic algorithm and simulated annealing to search global optimal solutions. The HGSA algorithm employs novel nearest-neighbor crossover operator which generates solutions based on nearest-neighbors so that the total travelled distance remains minimum possible. The proposed HGSA Algorithm was tested with 86 benchmark CVRP instances and the effectiveness of HGSA is shown by the results offered.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134182898","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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