Pub Date : 2011-06-28DOI: 10.1109/DMO.2011.5976506
Entisar E. Eljadi, Z. Othman
In order to evaluate the quality of UKM's NIDS, this paper presents the process of analyzing network traffic captured by Pusat Teknologi Maklumat (PTM) to detect whether it has any anomalies or not and to produce corresponding anomaly rules to be included in an update of UKM's NIDS. The network traffic data was collected using WireShark for three days, using the six most common network attributes. The experiment used three association rule data mining techniques known as Appriori, Fuzzy Appriori and FP-Growth based on two, five and ten second window slicing. Out of the four data-sets, data-sets one and two were detected to have anomalies. The results show that the Fuzzy Appriori algorithm presented the best quality result, while FP-Growth presented a faster time to reach a solution. The data-sets, which was pre-processed in the form of two second window slicing displayed better results. This research outlines the steps that can be utilized by an organization to capture and detect anomalies using association rule data mining techniques to enhance the quality their of NIDS.
为了评估UKM NIDS的质量，本文介绍了对Pusat Teknologi Maklumat (PTM)捕获的网络流量进行分析的过程，以检测其是否存在异常，并生成相应的异常规则，以包含在UKM的NIDS更新中。使用WireShark工具收集网络流量数据，收集时间为3天，使用了6种最常见的网络属性。实验使用了三种关联规则数据挖掘技术，即Appriori、模糊Appriori和基于2秒、5秒和10秒窗口切片的FP-Growth。在四个数据集中，数据集1和数据集2被检测到有异常。结果表明，Fuzzy Appriori算法的求解质量最好，FP-Growth算法的求解速度更快。以2秒窗口切片的形式进行预处理的数据集显示出更好的效果。本研究概述了组织可以使用关联规则数据挖掘技术捕获和检测异常的步骤，以提高NIDS的质量。
{"title":"Anomaly detection for PTM's network traffic using association rule","authors":"Entisar E. Eljadi, Z. Othman","doi":"10.1109/DMO.2011.5976506","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976506","url":null,"abstract":"In order to evaluate the quality of UKM's NIDS, this paper presents the process of analyzing network traffic captured by Pusat Teknologi Maklumat (PTM) to detect whether it has any anomalies or not and to produce corresponding anomaly rules to be included in an update of UKM's NIDS. The network traffic data was collected using WireShark for three days, using the six most common network attributes. The experiment used three association rule data mining techniques known as Appriori, Fuzzy Appriori and FP-Growth based on two, five and ten second window slicing. Out of the four data-sets, data-sets one and two were detected to have anomalies. The results show that the Fuzzy Appriori algorithm presented the best quality result, while FP-Growth presented a faster time to reach a solution. The data-sets, which was pre-processed in the form of two second window slicing displayed better results. This research outlines the steps that can be utilized by an organization to capture and detect anomalies using association rule data mining techniques to enhance the quality their of NIDS.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128547283","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976502
Norlela Samsudin, Mazidah Puteh, A. Hamdan
Advancement in information and technology facilities especially the Internet has changed the way we communicate and express opinions or sentiments on services or products that we consume. Opinion mining aims to automate the process of mining opinions into the positive or the negative views. It will benefit both the customers and the sellers in identifying the best product or service. Although there are researchers that explore new techniques of identifying the sentiment polarization, few works have been done on opinion mining created by the Malaysian reviewers. The same scenario happens to micro-text. Therefore in this study, we conduct an exploratory research on opinion mining of online movie reviews collected from several forums and blogs written by the Malaysian. The experiment data are tested using machine learning classifiers i.e. Support VectorMachine, Naïve Baiyes and k-Nearest Neighbor. The result illustrates that the performance of these machine learning techniques without any preprocessing of the micro-texts or feature selection is quite low. Therefore additional steps are required in order to mine the opinions from these data.
{"title":"Bess or xbest: Mining the Malaysian online reviews","authors":"Norlela Samsudin, Mazidah Puteh, A. Hamdan","doi":"10.1109/DMO.2011.5976502","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976502","url":null,"abstract":"Advancement in information and technology facilities especially the Internet has changed the way we communicate and express opinions or sentiments on services or products that we consume. Opinion mining aims to automate the process of mining opinions into the positive or the negative views. It will benefit both the customers and the sellers in identifying the best product or service. Although there are researchers that explore new techniques of identifying the sentiment polarization, few works have been done on opinion mining created by the Malaysian reviewers. The same scenario happens to micro-text. Therefore in this study, we conduct an exploratory research on opinion mining of online movie reviews collected from several forums and blogs written by the Malaysian. The experiment data are tested using machine learning classifiers i.e. Support VectorMachine, Naïve Baiyes and k-Nearest Neighbor. The result illustrates that the performance of these machine learning techniques without any preprocessing of the micro-texts or feature selection is quite low. Therefore additional steps are required in order to mine the opinions from these data.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128562024","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976504
Maznan Deraman, Abd Jalil Desa, Z. Othman
Network intrusion detection system (NIDS) commonly attributed to the task to mitigate network and security attacks that has potential to compromise the safety of a network resources and its information. Research in this area mainly focuses to improve the detection method in network traffic flow. Machine learning techniques had been widely used to analyze large datasets including network traffic. In order to develop a sound mechanism for NIDS detection tool, benchmark datasets is required to assist the data mining process. This paper presents the benchmark datasets available publicly for NIDS study such as KDDCup99, IES, pcapr and others. We use some popular machine learning tools to visualize the properties and characteristics of the benchmark datasets.
{"title":"Public domain datasets for optimizing network intrusion and machine learning approaches","authors":"Maznan Deraman, Abd Jalil Desa, Z. Othman","doi":"10.1109/DMO.2011.5976504","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976504","url":null,"abstract":"Network intrusion detection system (NIDS) commonly attributed to the task to mitigate network and security attacks that has potential to compromise the safety of a network resources and its information. Research in this area mainly focuses to improve the detection method in network traffic flow. Machine learning techniques had been widely used to analyze large datasets including network traffic. In order to develop a sound mechanism for NIDS detection tool, benchmark datasets is required to assist the data mining process. This paper presents the benchmark datasets available publicly for NIDS study such as KDDCup99, IES, pcapr and others. We use some popular machine learning tools to visualize the properties and characteristics of the benchmark datasets.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129814934","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976540
F. Ismail, Noor Elaiza Abd Khalid, Nordin Abu Bakar, Ropandi Mamat
The shortage of rubber wood (RW) supply has increased the demand to reduce its composition in the Medium Density Fiberboard (MDF). Oil palm biomass such as empty fruit bunch (EFB) has been proven to be an excellent substitute to RW. An accurate percentage combination of RW and EFB will produce a high quality MDF. An MDF needs to be tested in terms of mechanical and physical properties so that it meets the required standard. These tests are costly for they involve high amount of resources. The aim of this research is to optimize the properties of MDF so that quality-testing procedures can be reduced. A prediction model will be used to make predictions on the MDF properties. A stepwise multiple linear regression selects the predictor variables to be used as inputs to the input nodes. With these variables, the multilayer perceptron neural network with various training criteria will train the data and finally produce the prediction. The results produced have shown that some of the property tests can be omitted.
{"title":"Optimizing oil palm fiberboard properties using neural network","authors":"F. Ismail, Noor Elaiza Abd Khalid, Nordin Abu Bakar, Ropandi Mamat","doi":"10.1109/DMO.2011.5976540","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976540","url":null,"abstract":"The shortage of rubber wood (RW) supply has increased the demand to reduce its composition in the Medium Density Fiberboard (MDF). Oil palm biomass such as empty fruit bunch (EFB) has been proven to be an excellent substitute to RW. An accurate percentage combination of RW and EFB will produce a high quality MDF. An MDF needs to be tested in terms of mechanical and physical properties so that it meets the required standard. These tests are costly for they involve high amount of resources. The aim of this research is to optimize the properties of MDF so that quality-testing procedures can be reduced. A prediction model will be used to make predictions on the MDF properties. A stepwise multiple linear regression selects the predictor variables to be used as inputs to the input nodes. With these variables, the multilayer perceptron neural network with various training criteria will train the data and finally produce the prediction. The results produced have shown that some of the property tests can be omitted.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131948575","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976531
Majid Abdolrazzagh Nezhad, S. Abdullah
Most of the methods to solve job-shop scheduling problem (JSSP) are population-based and one of the strategies to reduce the time to reach the optimal solution is to produce an initial population that firstly has suitable distribution on space solution, secondly some of its points settle nearby to the optimal solution and lastly generate it in the shortest possible time. But since JSSP is one of the most difficult NP-complete problems and its space solution is complex, most of the previous researchers have preferred to utilize random methods or priority rules for producing initial population. In this paper, by mapping each schedule to a unique sequence of jobs on machines matrix (SJM), we have proposed the novel concept of plates, and have redefined and adapted concepts of tail and head path and have designed evaluator functions between SJM matrix and its corresponding schedule aiming at identifying gaps in the obtained schedule, we have proposed three novel initialization procedures. The proposed procedures have been run on 73 benchmark datasets and their results have been compared with some existing initialization procedures and even some approximation algorithms for solving JSSP. Based on this comparison, we have seen the proposed procedures have the significant advantage both in the quality-generated points and in the time producing them. The more interesting point in the implementation of proposed procedures on some datasets is that we see the best known solution in the produced initial population.
{"title":"Robust start for population-based algorithms solving job-shop scheduling problems","authors":"Majid Abdolrazzagh Nezhad, S. Abdullah","doi":"10.1109/DMO.2011.5976531","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976531","url":null,"abstract":"Most of the methods to solve job-shop scheduling problem (JSSP) are population-based and one of the strategies to reduce the time to reach the optimal solution is to produce an initial population that firstly has suitable distribution on space solution, secondly some of its points settle nearby to the optimal solution and lastly generate it in the shortest possible time. But since JSSP is one of the most difficult NP-complete problems and its space solution is complex, most of the previous researchers have preferred to utilize random methods or priority rules for producing initial population. In this paper, by mapping each schedule to a unique sequence of jobs on machines matrix (SJM), we have proposed the novel concept of plates, and have redefined and adapted concepts of tail and head path and have designed evaluator functions between SJM matrix and its corresponding schedule aiming at identifying gaps in the obtained schedule, we have proposed three novel initialization procedures. The proposed procedures have been run on 73 benchmark datasets and their results have been compared with some existing initialization procedures and even some approximation algorithms for solving JSSP. Based on this comparison, we have seen the proposed procedures have the significant advantage both in the quality-generated points and in the time producing them. The more interesting point in the implementation of proposed procedures on some datasets is that we see the best known solution in the produced initial population.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133183651","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976518
A. A. Ibrahim, A. Mohamed, H. Shareef, S. Ghoshal
In the modern industry, most of the equipment use semiconductor devices and microprocessors which are sensitive against power disturbances. Among power disturbances, voltage sags are considered as the most frequent type of disturbances in the field and their impact on sensitive loads is severe. However, to assess voltage sags, installation of power quality monitors (PQM) at all system buses is not economical. Thus, this study is carried out to develop a power quality monitor positioning algorithm to find the optimal number and placement of PQMs in both transmission and distribution systems. In this approach, first, the concept of topological monitor reach area is introduced. Then the binary particle swarm optimization hybridized with artificial immune system is used to solve multi-objective function in finding the optimal placement of PQMs. The proposed algorithm has been implemented and tested on the IEEE 30-bus and the 69-bus test systems to show the effectiveness of the proposed method for both transmission and distribution systems.
{"title":"Optimal power quality monitor placement in power systems based on particle swarm optimization and artificial immune system","authors":"A. A. Ibrahim, A. Mohamed, H. Shareef, S. Ghoshal","doi":"10.1109/DMO.2011.5976518","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976518","url":null,"abstract":"In the modern industry, most of the equipment use semiconductor devices and microprocessors which are sensitive against power disturbances. Among power disturbances, voltage sags are considered as the most frequent type of disturbances in the field and their impact on sensitive loads is severe. However, to assess voltage sags, installation of power quality monitors (PQM) at all system buses is not economical. Thus, this study is carried out to develop a power quality monitor positioning algorithm to find the optimal number and placement of PQMs in both transmission and distribution systems. In this approach, first, the concept of topological monitor reach area is introduced. Then the binary particle swarm optimization hybridized with artificial immune system is used to solve multi-objective function in finding the optimal placement of PQMs. The proposed algorithm has been implemented and tested on the IEEE 30-bus and the 69-bus test systems to show the effectiveness of the proposed method for both transmission and distribution systems.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130088512","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976510
A. Singh, Kumar Shubhankar, Vikram Pudi
In this paper we propose an efficient method to rank the research papers from various fields of research published in various conferences over the years. This ranking method is based on citation network. The importance of a research paper is captured well by the peer vote, which in this case is the research paper being cited in other research papers. Using a modified version of the PageRank algorithm, we rank the research papers, assigning each of them an authoritative score. Using the scores of the research papers calculated by above mentioned method, we formulate scores for conferences and authors and rank them as well. We have introduced a new metric in the algorithm which takes into account the time factor in ranking the research papers to reduce the bias against the recent papers which get less time for being studied and consequently cited by the researchers as compared to the older papers. Often a researcher is more interested in finding the top conferences in a particular year rather than the overall conference ranking. Considering the year of publication of the papers, in addition to the paper scores we also calculated the year-wise score of each conference by slight improvisation of the above mentioned algorithm.
{"title":"An efficient algorithm for ranking research papers based on citation network","authors":"A. Singh, Kumar Shubhankar, Vikram Pudi","doi":"10.1109/DMO.2011.5976510","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976510","url":null,"abstract":"In this paper we propose an efficient method to rank the research papers from various fields of research published in various conferences over the years. This ranking method is based on citation network. The importance of a research paper is captured well by the peer vote, which in this case is the research paper being cited in other research papers. Using a modified version of the PageRank algorithm, we rank the research papers, assigning each of them an authoritative score. Using the scores of the research papers calculated by above mentioned method, we formulate scores for conferences and authors and rank them as well. We have introduced a new metric in the algorithm which takes into account the time factor in ranking the research papers to reduce the bias against the recent papers which get less time for being studied and consequently cited by the researchers as compared to the older papers. Often a researcher is more interested in finding the top conferences in a particular year rather than the overall conference ranking. Considering the year of publication of the papers, in addition to the paper scores we also calculated the year-wise score of each conference by slight improvisation of the above mentioned algorithm.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123788949","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976511
Kumar Shubankar, A. Singh, Vikram Pudi
In this paper we introduce a novel and efficient approach to detect topics in a large corpus of research papers. With rapidly growing size of academic literature, the problem of topic detection has become a very challenging task. We present a unique approach that uses closed frequent keyword-set to form topics. Our approach also provides a natural method to cluster the research papers into hierarchical, overlapping clusters using topic as similarity measure. To rank the research papers in the topic cluster, we devise a modified PageRank algorithm that assigns an authoritative score to each research paper by considering the sub-graph in which the research paper appears. We test our algorithms on the DBLP dataset and experimentally show that our algorithms are fast, effective and scalable.
{"title":"A frequent keyword-set based algorithm for topic modeling and clustering of research papers","authors":"Kumar Shubankar, A. Singh, Vikram Pudi","doi":"10.1109/DMO.2011.5976511","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976511","url":null,"abstract":"In this paper we introduce a novel and efficient approach to detect topics in a large corpus of research papers. With rapidly growing size of academic literature, the problem of topic detection has become a very challenging task. We present a unique approach that uses closed frequent keyword-set to form topics. Our approach also provides a natural method to cluster the research papers into hierarchical, overlapping clusters using topic as similarity measure. To rank the research papers in the topic cluster, we devise a modified PageRank algorithm that assigns an authoritative score to each research paper by considering the sub-graph in which the research paper appears. We test our algorithms on the DBLP dataset and experimentally show that our algorithms are fast, effective and scalable.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114786266","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976539
Ehsan Toreini, M. Mehrnejad
Data clustering has been studied for a long time and every day trends are proposed for better outcomes in this field. One of the latest trends in this area is the application of Particle Swarm Optimization (PSO) in clustering which has good potential for improvements. In this paper, we consider a new fitness function for our PSO-based clustering method and compared it with the previous ones. Experimental results show that our method has better outcomes than the other ones.
{"title":"Clustering data with Particle Swarm Optimization using a new fitness","authors":"Ehsan Toreini, M. Mehrnejad","doi":"10.1109/DMO.2011.5976539","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976539","url":null,"abstract":"Data clustering has been studied for a long time and every day trends are proposed for better outcomes in this field. One of the latest trends in this area is the application of Particle Swarm Optimization (PSO) in clustering which has good potential for improvements. In this paper, we consider a new fitness function for our PSO-based clustering method and compared it with the previous ones. Experimental results show that our method has better outcomes than the other ones.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130030988","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976514
M.N.I. Sulaiman, Y. Choo, K. Chong
This work presents an approach based on the ant colony optimization technique to address the assembly line balancing problem. An improved ant colony optimization with look forward ant is proposed to solve the simple assembly line balancing problem of type 1 (SALBP-1). The proposed algorithm introduces an approach to dynamically assign the value of priority rule or heuristic information during the task selection phase by allowing the ant to look forward its direct successors during the consideration in selecting a task to be assigned into a workstation. The proposed algorithm is tested and compared with literature data sets and the result from the proposed algorithm shows competitive performance against them.
{"title":"Ant colony optimization with look forward ant in solving assembly line balancing problem","authors":"M.N.I. Sulaiman, Y. Choo, K. Chong","doi":"10.1109/DMO.2011.5976514","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976514","url":null,"abstract":"This work presents an approach based on the ant colony optimization technique to address the assembly line balancing problem. An improved ant colony optimization with look forward ant is proposed to solve the simple assembly line balancing problem of type 1 (SALBP-1). The proposed algorithm introduces an approach to dynamically assign the value of priority rule or heuristic information during the task selection phase by allowing the ant to look forward its direct successors during the consideration in selecting a task to be assigned into a workstation. The proposed algorithm is tested and compared with literature data sets and the result from the proposed algorithm shows competitive performance against them.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"2007 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125570512","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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