Pub Date : 2019-04-01DOI: 10.4018/IJNCR.2019040102
Soumen Atta, P. Mahapatra, A. Mukhopadhyay
A well-known combinatorial optimization problem, known as the uncapacitated facility location problem (UFLP) is considered in this article. A deterministic heuristic algorithm and a randomized heuristic algorithm are presented to solve UFLP. Though the proposed deterministic heuristic algorithm is very simple, it produces good solution for each instance of UFLP considered in this article. The main purpose of this article is to process all the data sets of UFLP available in the literature using a single algorithm. The proposed two algorithms are applied on these test instances of UFLP to determine their effectiveness. Here, the solution obtained from the proposed randomized algorithm is at least as good as the solution produced by the proposed deterministic algorithm. Hence, the proposed deterministic algorithm gives upper bound on the solution produced by the randomized algorithm. Although the proposed deterministic algorithm gives optimal results for most of the instances of UFLP, the randomized algorithm achieves optimal results for all the instances of UFLP considered in this article including those for which the deterministic algorithm fails to achieve the optimal solutions.
{"title":"Solving Uncapacitated Facility Location Problem Using Heuristic Algorithms","authors":"Soumen Atta, P. Mahapatra, A. Mukhopadhyay","doi":"10.4018/IJNCR.2019040102","DOIUrl":"https://doi.org/10.4018/IJNCR.2019040102","url":null,"abstract":"A well-known combinatorial optimization problem, known as the uncapacitated facility location problem (UFLP) is considered in this article. A deterministic heuristic algorithm and a randomized heuristic algorithm are presented to solve UFLP. Though the proposed deterministic heuristic algorithm is very simple, it produces good solution for each instance of UFLP considered in this article. The main purpose of this article is to process all the data sets of UFLP available in the literature using a single algorithm. The proposed two algorithms are applied on these test instances of UFLP to determine their effectiveness. Here, the solution obtained from the proposed randomized algorithm is at least as good as the solution produced by the proposed deterministic algorithm. Hence, the proposed deterministic algorithm gives upper bound on the solution produced by the randomized algorithm. Although the proposed deterministic algorithm gives optimal results for most of the instances of UFLP, the randomized algorithm achieves optimal results for all the instances of UFLP considered in this article including those for which the deterministic algorithm fails to achieve the optimal solutions.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132696706","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 : 2019-04-01DOI: 10.4018/IJNCR.2019040101
A. Gosain, Kavita Sachdeva
Materialized view selection (MVS) plays a vital role for efficiently making decisions in a data warehouse. This problem is NP-hard and constrained optimization problem. The authors have handled both the space and maintenance cost constraint using penalty functions. Three penalty function methods i.e. static, dynamic and adaptive penalty functions have been used for handling constraints and Backtracking Search Optimization algorithm (BSA) has been used for optimizing the total query processing cost. Experiments were conducted comparing the static, dynamic and adaptive penalty functions on varying the space constraint. The adaptive penalty function method yields the best results in terms of minimum query processing cost and achieves the optimality, scalability and feasibility of the problem on varying the lattice dimensions and on increasing the number of user queries. The authors proposed work has been compared with other evolutionary algorithms i.e. PSO and genetic algorithm and yields better results in terms of minimum total query processing cost of the materialized views.
{"title":"Handling Constraints Using Penalty Functions in Materialized View Selection","authors":"A. Gosain, Kavita Sachdeva","doi":"10.4018/IJNCR.2019040101","DOIUrl":"https://doi.org/10.4018/IJNCR.2019040101","url":null,"abstract":"Materialized view selection (MVS) plays a vital role for efficiently making decisions in a data warehouse. This problem is NP-hard and constrained optimization problem. The authors have handled both the space and maintenance cost constraint using penalty functions. Three penalty function methods i.e. static, dynamic and adaptive penalty functions have been used for handling constraints and Backtracking Search Optimization algorithm (BSA) has been used for optimizing the total query processing cost. Experiments were conducted comparing the static, dynamic and adaptive penalty functions on varying the space constraint. The adaptive penalty function method yields the best results in terms of minimum query processing cost and achieves the optimality, scalability and feasibility of the problem on varying the lattice dimensions and on increasing the number of user queries. The authors proposed work has been compared with other evolutionary algorithms i.e. PSO and genetic algorithm and yields better results in terms of minimum total query processing cost of the materialized views.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121949508","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 : 2019-04-01DOI: 10.4018/IJNCR.2019040104
S. Chakraborty, Jyotika Doshi
Results of OLAP queries for strategic decision making are generated using warehouse data. For frequent queries, processing overhead increases as same results are generated by traversing through huge volume of warehouse data. Authors suggest saving time for frequent queries by storing them in a relational database referred as MQDB, along with its result and metadata information. Incremental updates for synonymous materialized queries are done using data marts. This article focusses on saving processing time for non-synonymous queries with differed criteria. Criteria is the query condition specified with ‘where' or a ‘having' clause apart from equijoin condition. Defined rules will determine if new results can be derived from existing stored results. If criteria of fired query are a subset of criteria in stored query, results are extracted from existing results using MINUS operation. When criteria are a superset of stored query criteria, new results are appended to existing results using the UNION operation.
{"title":"Reducing Query Processing Time for Non-Synonymous Materialized Queries With Differed Criteria","authors":"S. Chakraborty, Jyotika Doshi","doi":"10.4018/IJNCR.2019040104","DOIUrl":"https://doi.org/10.4018/IJNCR.2019040104","url":null,"abstract":"Results of OLAP queries for strategic decision making are generated using warehouse data. For frequent queries, processing overhead increases as same results are generated by traversing through huge volume of warehouse data. Authors suggest saving time for frequent queries by storing them in a relational database referred as MQDB, along with its result and metadata information. Incremental updates for synonymous materialized queries are done using data marts. This article focusses on saving processing time for non-synonymous queries with differed criteria. Criteria is the query condition specified with ‘where' or a ‘having' clause apart from equijoin condition. Defined rules will determine if new results can be derived from existing stored results. If criteria of fired query are a subset of criteria in stored query, results are extracted from existing results using MINUS operation. When criteria are a superset of stored query criteria, new results are appended to existing results using the UNION operation.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133660313","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 : 2019-01-01DOI: 10.4018/IJNCR.2019010103
Kaustav Sengupta, Sovan Saha, P. Chatterjee, M. Kundu, M. Nasipuri, Subhadip Basu
Essential protein identification is an important factor to inspect the mechanisms of disease progression and to identify drug targets. With the advancement of high throughput genome sequencing projects, a bulk of protein data is available where the analysis of interaction pattern, functional annotation and characterization are necessary for detecting proteins' essentiality in network level. A set of centrality measure has been used to identify the highly connected proteins or hubs. From recent studies, it is observed that the majority of hubs are considered to be essential proteins. In this article, a method EPIN_Pred is proposed where a combination of several centrality measures is used to find the hub and non-hub proteins. Using the cohesiveness property, overlapping topological clusters are found. Using gene ontology (GO) terms, these topological clusters are again combined, if required. The performance of EPIN_Pred is also found to be superior when compared to other state-of-the-art methods.
{"title":"Identification of Essential Proteins by Detecting Topological and Functional Clusters in Protein Interaction Network of Saccharomyces Cerevisiae","authors":"Kaustav Sengupta, Sovan Saha, P. Chatterjee, M. Kundu, M. Nasipuri, Subhadip Basu","doi":"10.4018/IJNCR.2019010103","DOIUrl":"https://doi.org/10.4018/IJNCR.2019010103","url":null,"abstract":"Essential protein identification is an important factor to inspect the mechanisms of disease progression and to identify drug targets. With the advancement of high throughput genome sequencing projects, a bulk of protein data is available where the analysis of interaction pattern, functional annotation and characterization are necessary for detecting proteins' essentiality in network level. A set of centrality measure has been used to identify the highly connected proteins or hubs. From recent studies, it is observed that the majority of hubs are considered to be essential proteins. In this article, a method EPIN_Pred is proposed where a combination of several centrality measures is used to find the hub and non-hub proteins. Using the cohesiveness property, overlapping topological clusters are found. Using gene ontology (GO) terms, these topological clusters are again combined, if required. The performance of EPIN_Pred is also found to be superior when compared to other state-of-the-art methods.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116352755","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 : 2018-10-01DOI: 10.4018/IJNCR.2018100102
Saggurthi Kishor Babu, S. Vasavi
Predictive analytics can forecast trends, determines statistical probabilities and to act upon fraud and security threats for big data applications. Predictive analytics as a service (PAaaS) framework based upon ensemble model that uses Gaussian process with varying hyper parameters, Artificial Neural Networks, Auto Regression algorithm and Gaussian process is discussed in the authors' earlier works. Such framework can make in-depth statistical insights of data that helps in decision making process. This article reports the presentation layer of PAaaS for real time visualization and analytical reporting of these statistical insights. Result from various feature engineering strategies for predictive analytics is visualized in specific to type of feature engineering strategy and visualization technique using Tableau.
{"title":"Visualization of Feature Engineering Strategies for Predictive Analytics","authors":"Saggurthi Kishor Babu, S. Vasavi","doi":"10.4018/IJNCR.2018100102","DOIUrl":"https://doi.org/10.4018/IJNCR.2018100102","url":null,"abstract":"Predictive analytics can forecast trends, determines statistical probabilities and to act upon fraud and security threats for big data applications. Predictive analytics as a service (PAaaS) framework based upon ensemble model that uses Gaussian process with varying hyper parameters, Artificial Neural Networks, Auto Regression algorithm and Gaussian process is discussed in the authors' earlier works. Such framework can make in-depth statistical insights of data that helps in decision making process. This article reports the presentation layer of PAaaS for real time visualization and analytical reporting of these statistical insights. Result from various feature engineering strategies for predictive analytics is visualized in specific to type of feature engineering strategy and visualization technique using Tableau.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131787027","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 : 2018-10-01DOI: 10.4018/IJNCR.2018100101
A. K. Pal, I. Naskar, Sampa Paul
In model reference adaptive controller (MRACs), the adaptive gain of the controller is varied according to the process dynamic variation as it is directly related with the system stability. In MRAC, there is no provision of an automatic selection of adaptive gain and adaptation rate. To get rid of this problem and for the automatic selection of adaptive gain, a fuzzy-based scheme is presented in this article. In the proposed fuzzy-based technique, the controller output gain is illustrated as the function of input process parameters, which is continuously amended for any process parameter variations. A set-point modulation scheme is also incorporated to tackle the undesired process parameter variations and to improve performance indices of the process under control. The performance of the proposed set-point modulated fuzzy-based model reference adaptive controller (SFMRAC) is demonstrated on different second order linear, marginally stable and nonlinear models. The scheme is also explored on a real-time twin arm overhead crane for transport of material without pendulation.
{"title":"Fuzzy-based Gain Adaptive Scheme for Set-Point Modulated Model Reference Adaptive Controller","authors":"A. K. Pal, I. Naskar, Sampa Paul","doi":"10.4018/IJNCR.2018100101","DOIUrl":"https://doi.org/10.4018/IJNCR.2018100101","url":null,"abstract":"In model reference adaptive controller (MRACs), the adaptive gain of the controller is varied according to the process dynamic variation as it is directly related with the system stability. In MRAC, there is no provision of an automatic selection of adaptive gain and adaptation rate. To get rid of this problem and for the automatic selection of adaptive gain, a fuzzy-based scheme is presented in this article. In the proposed fuzzy-based technique, the controller output gain is illustrated as the function of input process parameters, which is continuously amended for any process parameter variations. A set-point modulation scheme is also incorporated to tackle the undesired process parameter variations and to improve performance indices of the process under control. The performance of the proposed set-point modulated fuzzy-based model reference adaptive controller (SFMRAC) is demonstrated on different second order linear, marginally stable and nonlinear models. The scheme is also explored on a real-time twin arm overhead crane for transport of material without pendulation.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126747146","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 : 2018-10-01DOI: 10.4018/IJNCR.2018100104
Vaasudev Narayanan, B. Parsi
Local feature description forms an integral part of texture classification, image recognition, and face recognition. In this paper, the authors propose Center Symmetric Local Ternary Mapped Patterns (CS-LTMP) and eXtended Center Symmetric Local Ternary Mapped Patterns (XCS-LTMP) for local description of images. They combine the strengths of Center Symmetric Local Ternary Pattern (CS-LTP) which uses ternary codes and Center Symmetric Local Mapped Pattern (CS-LMP) which captures the nuances between images to make the CS-LTMP. Similarly, the auhtors combined CS-LTP and eXtended Center Symmetric Local Mapped Pattern (XCS-LMP) to form eXtended Center Symmetric Local Ternary Mapped Pattern (XCS-LTMP). They have conducted their experiments on the CIFAR10 dataset and show that their proposed methods perform significantly better than their direct competitors.
{"title":"Center Symmetric Local Descriptors for Image Classification","authors":"Vaasudev Narayanan, B. Parsi","doi":"10.4018/IJNCR.2018100104","DOIUrl":"https://doi.org/10.4018/IJNCR.2018100104","url":null,"abstract":"Local feature description forms an integral part of texture classification, image recognition, and face recognition. In this paper, the authors propose Center Symmetric Local Ternary Mapped Patterns (CS-LTMP) and eXtended Center Symmetric Local Ternary Mapped Patterns (XCS-LTMP) for local description of images. They combine the strengths of Center Symmetric Local Ternary Pattern (CS-LTP) which uses ternary codes and Center Symmetric Local Mapped Pattern (CS-LMP) which captures the nuances between images to make the CS-LTMP. Similarly, the auhtors combined CS-LTP and eXtended Center Symmetric Local Mapped Pattern (XCS-LMP) to form eXtended Center Symmetric Local Ternary Mapped Pattern (XCS-LTMP). They have conducted their experiments on the CIFAR10 dataset and show that their proposed methods perform significantly better than their direct competitors.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130377899","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 : 2018-10-01DOI: 10.4018/IJNCR.2018100103
N. Kumar, Sunny Behal
Face recognition is considered as one of toughest and most crucial leading domains of digital image processing. The human brain also uses a similar kind of technique for face recognition. When scrutinizing a face, the human brain signifies the result. Aside from AN automatic processing system, this technique is very sophisticated, owing to the image variations on account of the picture varieties in as far as area, size, articulation, and stance. In this article, the authors have used the options of native binary pattern and uniform native binary pattern for face recognition. They compute a number of classifiers on publicly available benchmarked ORL image databases to validate the proposed approach. The results clearly show that the proposed LBP-piece shrewd strategy has outperformed the traditional LBP system.
{"title":"An Improved LBP Blockwise Method for Face Recognition","authors":"N. Kumar, Sunny Behal","doi":"10.4018/IJNCR.2018100103","DOIUrl":"https://doi.org/10.4018/IJNCR.2018100103","url":null,"abstract":"Face recognition is considered as one of toughest and most crucial leading domains of digital image processing. The human brain also uses a similar kind of technique for face recognition. When scrutinizing a face, the human brain signifies the result. Aside from AN automatic processing system, this technique is very sophisticated, owing to the image variations on account of the picture varieties in as far as area, size, articulation, and stance. In this article, the authors have used the options of native binary pattern and uniform native binary pattern for face recognition. They compute a number of classifiers on publicly available benchmarked ORL image databases to validate the proposed approach. The results clearly show that the proposed LBP-piece shrewd strategy has outperformed the traditional LBP system.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122325463","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 : 2018-07-01DOI: 10.4018/IJNCR.2018070103
Bharat Singh, S. Urooj
Controlled drug delivery systems (DDS's) is an electromechanical system that supports the injection of a therapeutic drug intravenously into a patient's body and easily controls the infusion rate of patient's drug, blood pressure, and time of drug release. The controlled operation of mean arterial blood pressure (MABP) and cardiac output (CO) is highly desired in clinical operations. Different methods have been proposed for controlling MABP, all methods have certain disadvantages according to patient model. In this article, the authors propose blood pressure control using integral reinforcement learning based fuzzy inference systems (IRLFI) based on parameter estimation techniques and have compared this method in terms of integral squared error (ISE), integral absolute error (IAE), integral time-weighed absolute error (ITAE), root mean square error (RMSE), convergence time (CT).
{"title":"Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation","authors":"Bharat Singh, S. Urooj","doi":"10.4018/IJNCR.2018070103","DOIUrl":"https://doi.org/10.4018/IJNCR.2018070103","url":null,"abstract":"Controlled drug delivery systems (DDS's) is an electromechanical system that supports the injection of a therapeutic drug intravenously into a patient's body and easily controls the infusion rate of patient's drug, blood pressure, and time of drug release. The controlled operation of mean arterial blood pressure (MABP) and cardiac output (CO) is highly desired in clinical operations. Different methods have been proposed for controlling MABP, all methods have certain disadvantages according to patient model. In this article, the authors propose blood pressure control using integral reinforcement learning based fuzzy inference systems (IRLFI) based on parameter estimation techniques and have compared this method in terms of integral squared error (ISE), integral absolute error (IAE), integral time-weighed absolute error (ITAE), root mean square error (RMSE), convergence time (CT).","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114179163","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 : 2018-07-01DOI: 10.4018/IJNCR.2018070104
P. Parwekar
In wireless sensor networks (WSNs), consumption of energy is the major challenging issue. If the data is transmitted directly from the node to the base station, it leads to more transmissions and energy consumed also increases if the communication distance is longer. In such cases, to reduce the longer communication distances and to reduce the number of transmissions, a clustering technique is employed. Another way to reduce the energy consumed is to reduce the transmission from node to CH or from CH to BS. Reducing the transmission distance is a NP-Hard problem. So, optimization techniques can be used effectively to solve such problems. In this article, is the implementation of a social group optimization (SGO) to reduce the transmission distance and to allow the nodes to consume less energy. The performance of SGO is compared with GA and PSO and the results show that SGO outperforms in terms of fitness and energy.
{"title":"SGO A New Approach for Energy Efficient Clustering in WSN","authors":"P. Parwekar","doi":"10.4018/IJNCR.2018070104","DOIUrl":"https://doi.org/10.4018/IJNCR.2018070104","url":null,"abstract":"In wireless sensor networks (WSNs), consumption of energy is the major challenging issue. If the data is transmitted directly from the node to the base station, it leads to more transmissions and energy consumed also increases if the communication distance is longer. In such cases, to reduce the longer communication distances and to reduce the number of transmissions, a clustering technique is employed. Another way to reduce the energy consumed is to reduce the transmission from node to CH or from CH to BS. Reducing the transmission distance is a NP-Hard problem. So, optimization techniques can be used effectively to solve such problems. In this article, is the implementation of a social group optimization (SGO) to reduce the transmission distance and to allow the nodes to consume less energy. The performance of SGO is compared with GA and PSO and the results show that SGO outperforms in terms of fitness and energy.","PeriodicalId":369881,"journal":{"name":"Int. J. Nat. Comput. Res.","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131840446","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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