Pub Date : 2012-05-29DOI: 10.1109/ICNC.2012.6234713
Xin Xu, Yanheng Liu, Aimin Wang, G. Wang, Huiling Chen
This paper proposes an adaptive multi-objective bacterial swarm optimizer (AMBSO) for multi-objective problems. The proposed AMBSO method implements the search for Pareto optimal set of multi-objective optimization problems. The AMBSO has been compared with the MBFO over a test suite of five ZDT numerical benchmarks with respect to the two performance measures: Generational Distance and Diversity Measure. The simulation results show that the AMBSO is able to find a much better Pareto front solutions.
{"title":"An adaptive multi-objective bacterial swarm optimzer","authors":"Xin Xu, Yanheng Liu, Aimin Wang, G. Wang, Huiling Chen","doi":"10.1109/ICNC.2012.6234713","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234713","url":null,"abstract":"This paper proposes an adaptive multi-objective bacterial swarm optimizer (AMBSO) for multi-objective problems. The proposed AMBSO method implements the search for Pareto optimal set of multi-objective optimization problems. The AMBSO has been compared with the MBFO over a test suite of five ZDT numerical benchmarks with respect to the two performance measures: Generational Distance and Diversity Measure. The simulation results show that the AMBSO is able to find a much better Pareto front solutions.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121720153","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234702
Yuan Jing, Min-fang Qi, Zhong-guang Fu
The calorific value of coal is an important factor for the economic operation of coal fired power plant. However calorific value is tremendous difference between the different coal, and even if coal is from the same mine. Restricted by the coal market, most of coal fired power plants can not burn the designed-coal by now in China. The properties of coal as received are changing so frequently that pulverized coal firing is always with the unexpected condition. Therefore, the researches on the on-line prediction of calorific value of coal has a profound significance for the economic operation of power plants. Aiming at the problem of uncertainty of calorific value of coal, a soft measurement model for calorific value of coal is proposed based on the RBF neural network. And combined with the thought of k-cross validation, the genetic algorithm constructed a fitness function to optimize the RBF network parameters. It is shown by an example that the optimized model is concise and accurate, with good training accuracy and generalization ability. The model could provide a good guidance for the calculation of the calorific value of coal and optimization operation of coal fired power plants.
{"title":"Prediction of coal calorific value based on the RBF neural network optimized by genetic algorithm","authors":"Yuan Jing, Min-fang Qi, Zhong-guang Fu","doi":"10.1109/ICNC.2012.6234702","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234702","url":null,"abstract":"The calorific value of coal is an important factor for the economic operation of coal fired power plant. However calorific value is tremendous difference between the different coal, and even if coal is from the same mine. Restricted by the coal market, most of coal fired power plants can not burn the designed-coal by now in China. The properties of coal as received are changing so frequently that pulverized coal firing is always with the unexpected condition. Therefore, the researches on the on-line prediction of calorific value of coal has a profound significance for the economic operation of power plants. Aiming at the problem of uncertainty of calorific value of coal, a soft measurement model for calorific value of coal is proposed based on the RBF neural network. And combined with the thought of k-cross validation, the genetic algorithm constructed a fitness function to optimize the RBF network parameters. It is shown by an example that the optimized model is concise and accurate, with good training accuracy and generalization ability. The model could provide a good guidance for the calculation of the calorific value of coal and optimization operation of coal fired power plants.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122770048","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234749
Yan Deng, Haiying Wang
Reservoir thickness is an important parameter in the description and simulation of reservoir. The principle and method of the Support Vector Machines are introduced in this paper. Based on the previous study of seismic interpretation, 100 sets of data of the five seismic attributes and the reservoir thickness in a work area are used as the example for predicting the reservoir thickness. The results prove that this method may throw important light on the predicting and computing the reservoir thickness.
{"title":"The Support Vector Machines for predicting the reservoir thickness","authors":"Yan Deng, Haiying Wang","doi":"10.1109/ICNC.2012.6234749","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234749","url":null,"abstract":"Reservoir thickness is an important parameter in the description and simulation of reservoir. The principle and method of the Support Vector Machines are introduced in this paper. Based on the previous study of seismic interpretation, 100 sets of data of the five seismic attributes and the reservoir thickness in a work area are used as the example for predicting the reservoir thickness. The results prove that this method may throw important light on the predicting and computing the reservoir thickness.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125397404","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234603
Qianzhong Xiang, Hongga Li, B. Huang, Rongrong Li
Dangerous goods (DGs) can significantly affect the human and nature if they are exposed to the environment without any protection. This situation is likely to occur when accidents happen during the transportation process. Especially in large cities, due to high population density and complex traffic network, the transportation of GDs has to pass through densely populated areas or other sensitive districts. So only considering one traditional objective in routing planning, such as the shortest length of route or lowest cost, can no longer meet our needs. There is an urgent need to review and improve the way of route optimization for DGs transportation. This paper develops a multi-objective model for the determination of optimal routes. In this model, three conflicting objectives are considered. They are total travelling time, accident probability and population exposure risk. For settling this model, an improved ant colony optimization (ACO) is introduced with a novel multi-objective decision method named MAXMIN. With the support of geographical information system (GIS), a case study of Hong Kong is carried out for the transportation of DGs. The experimental results show the proposed approach is feasible and effective.
{"title":"Improved ant colony optimization for multi-objective route planning of dangerous goods","authors":"Qianzhong Xiang, Hongga Li, B. Huang, Rongrong Li","doi":"10.1109/ICNC.2012.6234603","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234603","url":null,"abstract":"Dangerous goods (DGs) can significantly affect the human and nature if they are exposed to the environment without any protection. This situation is likely to occur when accidents happen during the transportation process. Especially in large cities, due to high population density and complex traffic network, the transportation of GDs has to pass through densely populated areas or other sensitive districts. So only considering one traditional objective in routing planning, such as the shortest length of route or lowest cost, can no longer meet our needs. There is an urgent need to review and improve the way of route optimization for DGs transportation. This paper develops a multi-objective model for the determination of optimal routes. In this model, three conflicting objectives are considered. They are total travelling time, accident probability and population exposure risk. For settling this model, an improved ant colony optimization (ACO) is introduced with a novel multi-objective decision method named MAXMIN. With the support of geographical information system (GIS), a case study of Hong Kong is carried out for the transportation of DGs. The experimental results show the proposed approach is feasible and effective.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126133126","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234576
Yi Chen, A. Narayanan, Shaoning Pang, B. Tao
Malware is currently a major threat to information and computer security, with the volume and growing diversity of its variants causing major problems to traditional security defenses. Software patches and upgrades to anti-viral packages are typically released only after the malware's key characteristics have been identified through infection, by which time it may be too late to protect systems. Sequence analysis is widely used in bioinformatics for revealing the genetic diversity of organisms and annotating gene functions. This paper adopts a new approach to the problem of malware recognition, which is to use multiple sequence alignment techniques from bioinformatics to align variable length computer viral and worm code so that core, invariant regions of the code occupy fixed positions in the alignment patterns. Data mining (ANNs, symbolic rule extraction) can then be used to learn the critical features that help to determine into which class the aligned patterns fall. Experimental results demonstrate the feasibility of our novel approach for identifying malware code through multiple sequence alignment followed by analysis by ANNs and symbolic rule extraction methods.
{"title":"Multiple sequence alignment and artificial neural networks for malicious software detection","authors":"Yi Chen, A. Narayanan, Shaoning Pang, B. Tao","doi":"10.1109/ICNC.2012.6234576","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234576","url":null,"abstract":"Malware is currently a major threat to information and computer security, with the volume and growing diversity of its variants causing major problems to traditional security defenses. Software patches and upgrades to anti-viral packages are typically released only after the malware's key characteristics have been identified through infection, by which time it may be too late to protect systems. Sequence analysis is widely used in bioinformatics for revealing the genetic diversity of organisms and annotating gene functions. This paper adopts a new approach to the problem of malware recognition, which is to use multiple sequence alignment techniques from bioinformatics to align variable length computer viral and worm code so that core, invariant regions of the code occupy fixed positions in the alignment patterns. Data mining (ANNs, symbolic rule extraction) can then be used to learn the critical features that help to determine into which class the aligned patterns fall. Experimental results demonstrate the feasibility of our novel approach for identifying malware code through multiple sequence alignment followed by analysis by ANNs and symbolic rule extraction methods.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126286214","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234661
Wei Gao
The computation of slope stability is always a very important work for researchers and engineers in this field. The one key issue to solve this problem is the searching of critical slip surface. Generally, the searching of critical slip surface is a very typical complicated continuous optimization problem. To solve this problem very well, firstly, combing the artificial immune system algorithm and evolutionary algorithm with continuous ant colony algorithm, one new bionics algorithm for continuous function optimization which is called immunized continuous ant colony algorithm is proposed, secondly, combing immunized continuous ant colony algorithm with limit equilibrium analysis, one new global optimization algorithm for critical slip surface searching is proposed. At last, through a typical numerical example-Association for Computer Aided Design Society-Australia (ACADS) example and one engineering example-one highway slope, this new method is verified. The results show that, using the new algorithm, the searched slip surface will be coincided with the measured slip surface very well, and the stability safety factor will also be agree with the actual situation.
{"title":"Searching the critical slip surface of slope based on new bionics algorithm","authors":"Wei Gao","doi":"10.1109/ICNC.2012.6234661","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234661","url":null,"abstract":"The computation of slope stability is always a very important work for researchers and engineers in this field. The one key issue to solve this problem is the searching of critical slip surface. Generally, the searching of critical slip surface is a very typical complicated continuous optimization problem. To solve this problem very well, firstly, combing the artificial immune system algorithm and evolutionary algorithm with continuous ant colony algorithm, one new bionics algorithm for continuous function optimization which is called immunized continuous ant colony algorithm is proposed, secondly, combing immunized continuous ant colony algorithm with limit equilibrium analysis, one new global optimization algorithm for critical slip surface searching is proposed. At last, through a typical numerical example-Association for Computer Aided Design Society-Australia (ACADS) example and one engineering example-one highway slope, this new method is verified. The results show that, using the new algorithm, the searched slip surface will be coincided with the measured slip surface very well, and the stability safety factor will also be agree with the actual situation.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129982946","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234722
Yingchun Zhang, Juan Cao, Bohong Su
We propose a robust method based on genetic algorithm for the estimation of the motion between two successive overlapping images, a classic problem in computer vision. To calculate the motion parameters encoded as a chromosome, we employed roulette wheel selection and total arithmetic crossover and developed a novel adaptive mutation operator. The experimental results show that the normalized registration error of the final solution exhibits a significant improvement over those obtained by direct search approaches to such problems. Also, in contrast to other popular approaches such as the least-squares and Levenberg-Marquardt algorithm, the proposed method can escape from local extrema and can potentially produce the global optimum.
{"title":"Robust motion estimation for overlapping images via genetic algorithm","authors":"Yingchun Zhang, Juan Cao, Bohong Su","doi":"10.1109/ICNC.2012.6234722","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234722","url":null,"abstract":"We propose a robust method based on genetic algorithm for the estimation of the motion between two successive overlapping images, a classic problem in computer vision. To calculate the motion parameters encoded as a chromosome, we employed roulette wheel selection and total arithmetic crossover and developed a novel adaptive mutation operator. The experimental results show that the normalized registration error of the final solution exhibits a significant improvement over those obtained by direct search approaches to such problems. Also, in contrast to other popular approaches such as the least-squares and Levenberg-Marquardt algorithm, the proposed method can escape from local extrema and can potentially produce the global optimum.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129576766","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234525
Lina Wu, Yaping Huang, Wei Sun, Jianyu Ke
Image categorization is an important issue in computer vision. The bag-of-visual words(BOV) model which ignores spatial restriction of local features has gained state-of-the-art performance in recent years. The basic BOV model uses k-means to form codebook. As sparse codes can better represent local features, we use sparse codes of SIFT features instead of k-means to form codebook. Additional, as local features in most categories have spatial dependence in real world, this paper proposed to use visual word pairs to represent the spatial information between words. To reduce the complexity both in time and storage, we add word pairs dynamically. Our experiments show that our algorithm can improve the categorization performance.
{"title":"Create visual word pairs dynamically based on sparse codes of SIFT features for image categorization","authors":"Lina Wu, Yaping Huang, Wei Sun, Jianyu Ke","doi":"10.1109/ICNC.2012.6234525","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234525","url":null,"abstract":"Image categorization is an important issue in computer vision. The bag-of-visual words(BOV) model which ignores spatial restriction of local features has gained state-of-the-art performance in recent years. The basic BOV model uses k-means to form codebook. As sparse codes can better represent local features, we use sparse codes of SIFT features instead of k-means to form codebook. Additional, as local features in most categories have spatial dependence in real world, this paper proposed to use visual word pairs to represent the spatial information between words. To reduce the complexity both in time and storage, we add word pairs dynamically. Our experiments show that our algorithm can improve the categorization performance.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127294871","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234698
Chunmei Xu, Hao Zhang, D. Peng
A support vector machine (SVM) is presented for diagnosing the fault of the turbine generator unit. The SVM is based on the statistical learning theory and the structural risk minimization principle. It not only has greater generalization ability, but also a better solution to the small sample learning classification problems. In the case of limited feature information, SVM can explore furthest the classification of knowledge implicit in the sample data, and thus achieve better classification results. The simulation results show that the proposed method can effectively diagnose the vibration fault of turbine generator, and has good application prospects.
{"title":"Study of fault diagnosis based on SVM for turbine generator unit","authors":"Chunmei Xu, Hao Zhang, D. Peng","doi":"10.1109/ICNC.2012.6234698","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234698","url":null,"abstract":"A support vector machine (SVM) is presented for diagnosing the fault of the turbine generator unit. The SVM is based on the statistical learning theory and the structural risk minimization principle. It not only has greater generalization ability, but also a better solution to the small sample learning classification problems. In the case of limited feature information, SVM can explore furthest the classification of knowledge implicit in the sample data, and thus achieve better classification results. The simulation results show that the proposed method can effectively diagnose the vibration fault of turbine generator, and has good application prospects.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121901959","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 : 2012-05-29DOI: 10.1109/ICNC.2012.6234509
W. Zhou, Chunhua Liu, Hongbing Liu
Localization of sensor nodes is essential for wireless sensor network when it is applied to the special applications. We formed two models to estimate the location of sensor nodes, CART-based localization and SVMs-based Localization. During the training process, the received signal strength of the reference nodes is selected as the input of two models and the location information is regarded as the output of two models. During the localization process, the decision trees of CART and support vector machines are used to estimate the location of blindfolded nodes. We demonstrate the practicality and feasibility of the two models through simulations in the 100m×100m area.
{"title":"Comparison of CART-based localization and SVMs-based localization in WSN","authors":"W. Zhou, Chunhua Liu, Hongbing Liu","doi":"10.1109/ICNC.2012.6234509","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234509","url":null,"abstract":"Localization of sensor nodes is essential for wireless sensor network when it is applied to the special applications. We formed two models to estimate the location of sensor nodes, CART-based localization and SVMs-based Localization. During the training process, the received signal strength of the reference nodes is selected as the input of two models and the location information is regarded as the output of two models. During the localization process, the decision trees of CART and support vector machines are used to estimate the location of blindfolded nodes. We demonstrate the practicality and feasibility of the two models through simulations in the 100m×100m area.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122139824","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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