Scientific and technical report is an important document type with high intelligence value. But the resource distribution of different carrier forms of scientific and technical report is not integrated and the resource description is not deeply and specific enough to the report document type, which influences the information searching accuracy and efficiency for users. Functional Requirements for Bibliographic Records (FRBR), an emerging model in the bibliographic domain, provides interesting possibilities in terms of cataloguing, representation and semantic enrichment of bibliographic data. This study employs the FRBR conceptual model and entity-relationship analysis method to design in-depth descriptive metadata schema of scientific and technical report by analyzing the entities and mapping the bibliographic attributes corresponding to the characteristics of report, which can help to integrating and disclosing scientific and technical report resources.
科技报告是一种重要的文献类型，具有很高的情报价值。但科技报告不同载体形式的资源分布不整合，对报告文献类型的资源描述不够深入和具体，影响了用户信息检索的准确性和效率。书目记录功能需求(Functional Requirements for Bibliographic Records, FRBR)是书目领域的一个新兴模型，它在书目数据的编目、表示和语义丰富方面提供了有趣的可能性。本研究采用FRBR概念模型和实体关系分析方法，通过实体分析和对应报告特征的书目属性映射，设计深度描述性科技报告元数据模式，有助于科技报告资源的整合和披露。
{"title":"Constructing Metadata Schema of Scientific and Technical Report Based on FRBR","authors":"Xiaozhu Zou, Siyi Xiong, Zhi Li, P. Jiang","doi":"10.5539/CIS.V11N2P34","DOIUrl":"https://doi.org/10.5539/CIS.V11N2P34","url":null,"abstract":"Scientific and technical report is an important document type with high intelligence value. But the resource distribution of different carrier forms of scientific and technical report is not integrated and the resource description is not deeply and specific enough to the report document type, which influences the information searching accuracy and efficiency for users. Functional Requirements for Bibliographic Records (FRBR), an emerging model in the bibliographic domain, provides interesting possibilities in terms of cataloguing, representation and semantic enrichment of bibliographic data. This study employs the FRBR conceptual model and entity-relationship analysis method to design in-depth descriptive metadata schema of scientific and technical report by analyzing the entities and mapping the bibliographic attributes corresponding to the characteristics of report, which can help to integrating and disclosing scientific and technical report resources.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"1 1","pages":"34-39"},"PeriodicalIF":0.0,"publicationDate":"2018-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91322937","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}
Sadia Yeasmin, Muhammad Abrar Hussain, Noor Yazdani Sikder, R. Rahman
Over the decades there is a high demand of a tool to identify the nutritional needs of the people of Bangladesh since it has an alarming rate of under nutrition among the countries of the world. This analysis has focused on the dissimilarity of diseases caused by malnutrition in different districts of Bangladesh. Among the 64 districts, there is no single one found where people have grown proper nutritional food habit. Low income and less knowledge are the triggering factors and the case is worse in the rural areas. In this research, a distributed enumerating framework for large data set is processed in big data models. Fuzzy logic has the ability to model the nutrition problem, in the way helping people to calculate the suitability between food calories and user’s profile. A Map Reduce-based K-nearest neighbor (mrK-NN) classifier has been applied in this research in order to classify data. We have designed a balanced model applying fuzzy logic and big data analysis on Hadoop concerning food habit, food nutrition and disease, especially for the rural people.
{"title":"Finding Nutritional Deficiency and Disease Pattern of Rural People Using Fuzzy Logic and Big Data Techniques on Hadoop","authors":"Sadia Yeasmin, Muhammad Abrar Hussain, Noor Yazdani Sikder, R. Rahman","doi":"10.5539/cis.v11n2p11","DOIUrl":"https://doi.org/10.5539/cis.v11n2p11","url":null,"abstract":"Over the decades there is a high demand of a tool to identify the nutritional needs of the people of Bangladesh since it has an alarming rate of under nutrition among the countries of the world. This analysis has focused on the dissimilarity of diseases caused by malnutrition in different districts of Bangladesh. Among the 64 districts, there is no single one found where people have grown proper nutritional food habit. Low income and less knowledge are the triggering factors and the case is worse in the rural areas. In this research, a distributed enumerating framework for large data set is processed in big data models. Fuzzy logic has the ability to model the nutrition problem, in the way helping people to calculate the suitability between food calories and user’s profile. A Map Reduce-based K-nearest neighbor (mrK-NN) classifier has been applied in this research in order to classify data. We have designed a balanced model applying fuzzy logic and big data analysis on Hadoop concerning food habit, food nutrition and disease, especially for the rural people.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"54 1","pages":"11-33"},"PeriodicalIF":0.0,"publicationDate":"2018-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89662807","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 article presents a shortest-path model of vehicle scheduling, which based on analyzing the application of data mining in vehicle scheduling model by referring research status of data mining and describing logistics distribution process. The article also provides an algorithmic support by making the Dijkstra algorithm of the shortest path model simple and rational.
{"title":"Vehicle Scheduling Model Based on Data Mining","authors":"Guohua Zhang, Ting Xie, Min Liu, Yang Liu","doi":"10.5539/cis.v11n1p104","DOIUrl":"https://doi.org/10.5539/cis.v11n1p104","url":null,"abstract":"The article presents a shortest-path model of vehicle scheduling, which based on analyzing the application of data mining in vehicle scheduling model by referring research status of data mining and describing logistics distribution process. The article also provides an algorithmic support by making the Dijkstra algorithm of the shortest path model simple and rational.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"13 1","pages":"104-107"},"PeriodicalIF":0.0,"publicationDate":"2018-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75292181","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 IEEE 802.15.4 standard defines the PHY and MAC layer specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs). With the proliferation of many time-critical applications with real-time delivery, low latency, and/or specific bandwidth requirements, Guaranteed Time Slots (GTS) are increasingly being used for reliable contention-free data transmission by nodes within beacon-enabled WPANs. To evaluate the performance of the 802.15.4 GTS management scheme, this paper introduces a new GTS simulation model for OMNeT++ / MiXiM. Our GTS model considers star-topology WPANs within the 2.4 GHz frequency band, and is in full conformance with the IEEE 802.15.4 – 2006 standard. To enable thorough investigation of the behaviors and impacts of different attacks against the 802.15.4 GTS mechanism, a new GTS attacks simulation model for OMNeT++ is also introduced in this paper. Our GTS attacks model is developed for OMNeT++ / NETA, and is integrated with our GTS model to provide a single inclusive OMNeT++ simulation model for both the GTS mechanism and all known-to-date attacks against it.
{"title":"A Simulation Model of IEEE 802.15.4 GTS Mechanism and GTS Attacks in OMNeT++ / MiXiM + NETA","authors":"Y. M. Amin, A. T. Abdel-Hamid","doi":"10.5539/cis.v11n1p78","DOIUrl":"https://doi.org/10.5539/cis.v11n1p78","url":null,"abstract":"The IEEE 802.15.4 standard defines the PHY and MAC layer specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs). With the proliferation of many time-critical applications with real-time delivery, low latency, and/or specific bandwidth requirements, Guaranteed Time Slots (GTS) are increasingly being used for reliable contention-free data transmission by nodes within beacon-enabled WPANs. To evaluate the performance of the 802.15.4 GTS management scheme, this paper introduces a new GTS simulation model for OMNeT++ / MiXiM. Our GTS model considers star-topology WPANs within the 2.4 GHz frequency band, and is in full conformance with the IEEE 802.15.4 – 2006 standard. To enable thorough investigation of the behaviors and impacts of different attacks against the 802.15.4 GTS mechanism, a new GTS attacks simulation model for OMNeT++ is also introduced in this paper. Our GTS attacks model is developed for OMNeT++ / NETA, and is integrated with our GTS model to provide a single inclusive OMNeT++ simulation model for both the GTS mechanism and all known-to-date attacks against it.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"24 1","pages":"78-89"},"PeriodicalIF":0.0,"publicationDate":"2018-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90059112","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 auditing services of the outsourced data, especially big data, have been an active research area recently. Many schemes of remotely data auditing (RDA) have been proposed. Both categories of RDA, which are Provable Data Possession (PDP) and Proof of Retrievability (PoR), mostly represent the core schemes for most researchers to derive new schemes that support additional capabilities such as batch and dynamic auditing. In this paper, we choose the most popular PDP schemes to be investigated due to the existence of many PDP techniques which are further improved to achieve efficient integrity verification. We firstly review the work of literature to form the required knowledge about the auditing services and related schemes. Secondly, we specify a methodology to be adhered to attain the research goals. Then, we define each selected PDP scheme and the auditing properties to be used to compare between the chosen schemes. Therefore, we decide, if possible, which scheme is optimal in handling big data auditing.
{"title":"Enhancing Big Data Auditing","authors":"Sara Alomari, Mona Alghamdi, F. Alotaibi","doi":"10.5539/cis.v11n1p90","DOIUrl":"https://doi.org/10.5539/cis.v11n1p90","url":null,"abstract":"The auditing services of the outsourced data, especially big data, have been an active research area recently. Many schemes of remotely data auditing (RDA) have been proposed. Both categories of RDA, which are Provable Data Possession (PDP) and Proof of Retrievability (PoR), mostly represent the core schemes for most researchers to derive new schemes that support additional capabilities such as batch and dynamic auditing. In this paper, we choose the most popular PDP schemes to be investigated due to the existence of many PDP techniques which are further improved to achieve efficient integrity verification. We firstly review the work of literature to form the required knowledge about the auditing services and related schemes. Secondly, we specify a methodology to be adhered to attain the research goals. Then, we define each selected PDP scheme and the auditing properties to be used to compare between the chosen schemes. Therefore, we decide, if possible, which scheme is optimal in handling big data auditing.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"37 1","pages":"90-97"},"PeriodicalIF":0.0,"publicationDate":"2018-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73115039","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}
In today's society has entered the era of big data, data of the diversity and the amount of data increases to the data storage and processing brought great challenges, Hadoop HDFS and MapReduce better solves the these two problems. Classical K-means algorithm is the most widely used one based on the partition of the clustering algorithm. At the completion of the cluster configuration based on, the k-means algorithm in cluster mode of operation principle and in the cluster mode realized kmeans algorithm, and the experimental results are research and analysis, summarized the k-means algorithm is run on the Hadoop platform's strengths and limitations.
{"title":"The Analysis and Implementation of the K - Means Algorithm Based on Hadoop Platform","authors":"L. Wei","doi":"10.5539/cis.v11n1p98","DOIUrl":"https://doi.org/10.5539/cis.v11n1p98","url":null,"abstract":"In today's society has entered the era of big data, data of the diversity and the amount of data increases to the data storage and processing brought great challenges, Hadoop HDFS and MapReduce better solves the these two problems. Classical K-means algorithm is the most widely used one based on the partition of the clustering algorithm. At the completion of the cluster configuration based on, the k-means algorithm in cluster mode of operation principle and in the cluster mode realized kmeans algorithm, and the experimental results are research and analysis, summarized the k-means algorithm is run on the Hadoop platform's strengths and limitations.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"1 1","pages":"98-103"},"PeriodicalIF":0.0,"publicationDate":"2018-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90707186","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}
Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.
{"title":"Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey","authors":"M. H. Abdi, G. Okeyo, R. Mwangi","doi":"10.5539/cis.v11n2p1","DOIUrl":"https://doi.org/10.5539/cis.v11n2p1","url":null,"abstract":"Collaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"126 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2018-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79156754","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 knowledge bases of the Web are fundamentally organized in ontologies in order to answer queries based on semantics. The ontologies learning process comprises three fundamental steps: creation of classes and relationships, population and evaluation. In this paper the focus includes the classes creation, by introducing a class validation proposal using clustering analysis. As case of study was selected a pedagogical domain, where a corpus was semi-automatically built, from articles written in Spanish published in Social Sciences. Moreover, a dictionary containing classes, concepts and synonyms was included in the experiments. Clustering analysis allowed to verify the concepts that the experts considered as the most important for the domain. For the case of study selected, the cluster analysis step reports clusters with the same instances that the clusters defined by the experts.
{"title":"A Class Validation Proposal of a Pedagogic Domain Ontology based on Clustering Analysis","authors":"Yuridiana Alemán, M. J. S. García, D. V. Ayala","doi":"10.5539/cis.v11n1p65","DOIUrl":"https://doi.org/10.5539/cis.v11n1p65","url":null,"abstract":"The knowledge bases of the Web are fundamentally organized in ontologies in order to answer queries based on semantics. The ontologies learning process comprises three fundamental steps: creation of classes and relationships, population and evaluation. In this paper the focus includes the classes creation, by introducing a class validation proposal using clustering analysis. As case of study was selected a pedagogical domain, where a corpus was semi-automatically built, from articles written in Spanish published in Social Sciences. Moreover, a dictionary containing classes, concepts and synonyms was included in the experiments. Clustering analysis allowed to verify the concepts that the experts considered as the most important for the domain. For the case of study selected, the cluster analysis step reports clusters with the same instances that the clusters defined by the experts.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"21 1","pages":"65-77"},"PeriodicalIF":0.0,"publicationDate":"2018-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73050266","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}
Ghaith Abdulsattar A. Jabbar Alkubaisi, S. S. Kamaruddin, H. Husni
Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naive Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naive Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.
{"title":"Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers","authors":"Ghaith Abdulsattar A. Jabbar Alkubaisi, S. S. Kamaruddin, H. Husni","doi":"10.5539/cis.v11n1p52","DOIUrl":"https://doi.org/10.5539/cis.v11n1p52","url":null,"abstract":"Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naive Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naive Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"19 1","pages":"52-64"},"PeriodicalIF":0.0,"publicationDate":"2018-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88193957","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}
Mahdi Bidar, S. Sadaoui, Malek Mouhoub, Mohsen Bidar
Exploitation and exploration are two main search strategies of every metaheuristic algorithm . However, the ratio between exploitation and exploration has a significant impact on the performance of these algorithms when dealing with optimization problems. In this study, we introduce an entire fuzzy system to tune efficiently and dynamically the firefly algorithm parameters in order to keep the exploration and exploitation in balance in each of the searching steps. This will prevent the firefly algorithm from being stuck in local optimal, a challenge issue in metaheuristic algorithms . To evaluate the quality of the solution returned by the fuzzy-based firefly algorithm, we conduct extensive experiments on a set of high and low dimensional benchmark functions as well as two constrained engineering problems. In this regard, we compare the improved firefly algorithm with the standard one and other famous metaheuristic algorithms. The experimental results demonstrate the superiority of the fuzzy-based firefly algorithm to standard firefly and also its comparability to other metaheuristic algorithms.
{"title":"Enhanced Firefly Algorithm Using Fuzzy Parameter Tuner","authors":"Mahdi Bidar, S. Sadaoui, Malek Mouhoub, Mohsen Bidar","doi":"10.5539/cis.v11n1p26","DOIUrl":"https://doi.org/10.5539/cis.v11n1p26","url":null,"abstract":"Exploitation and exploration are two main search strategies of every metaheuristic algorithm . However, the ratio between exploitation and exploration has a significant impact on the performance of these algorithms when dealing with optimization problems. In this study, we introduce an entire fuzzy system to tune efficiently and dynamically the firefly algorithm parameters in order to keep the exploration and exploitation in balance in each of the searching steps. This will prevent the firefly algorithm from being stuck in local optimal, a challenge issue in metaheuristic algorithms . To evaluate the quality of the solution returned by the fuzzy-based firefly algorithm, we conduct extensive experiments on a set of high and low dimensional benchmark functions as well as two constrained engineering problems. In this regard, we compare the improved firefly algorithm with the standard one and other famous metaheuristic algorithms. The experimental results demonstrate the superiority of the fuzzy-based firefly algorithm to standard firefly and also its comparability to other metaheuristic algorithms.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"23 4 1","pages":"26-51"},"PeriodicalIF":0.0,"publicationDate":"2018-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83667541","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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