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.2012.6234677
Guocheng Zou, Liping Jia, Jin Zou
In this paper, we address a class of multiobjective bilevel mixed linear integer programming in which the upper level is a multiobjective linear optimization problem, and the lower level is a single-objective linear programming. For this kind of problem, the leader's decision are represented by zero-one variables, and the follower's decision are represented by continuous variables. Using KKT condition, the lower level is transformed into a series of constraints for the upper level. Based on coding, crossover, mutation, fitness assignment method and select strategy, an improved random-weight genetic algorithm for multiobjective bilevel mixed linear integer programming is proposed. By designing benchmark problems and suitable transformation, the proposed algorithm is compared by an existed branch-bound algorithm.
{"title":"Random-weight based genetic algorithm for multiobjective bilevel mixed linear integer programming","authors":"Guocheng Zou, Liping Jia, Jin Zou","doi":"10.1109/ICNC.2012.6234677","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234677","url":null,"abstract":"In this paper, we address a class of multiobjective bilevel mixed linear integer programming in which the upper level is a multiobjective linear optimization problem, and the lower level is a single-objective linear programming. For this kind of problem, the leader's decision are represented by zero-one variables, and the follower's decision are represented by continuous variables. Using KKT condition, the lower level is transformed into a series of constraints for the upper level. Based on coding, crossover, mutation, fitness assignment method and select strategy, an improved random-weight genetic algorithm for multiobjective bilevel mixed linear integer programming is proposed. By designing benchmark problems and suitable transformation, the proposed algorithm is compared by an existed branch-bound algorithm.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"51 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":"125316508","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}
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.2012.6234722
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.2012.6234661
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.2012.6234775
Ruopeng Wang, Hongmin Xu, Hong Shi, Xu You
In this paper, the infinite norm SVM is considered and a novel smoothing approximation function for Support Vector Machine is proposed in attempt to overcome some drawbacks of the former method which are complex, subtle, and sometimes difficult to implement. Firstly, we use Karush-Kuhn-Tucker complementary condition in optimization theory, and the unconstrained non-differentiable optimization model is built. Then the smooth approximation algorithm based on differentiable function is given. Finally, the paper trains the data sets with standard unconstraint optimization method. This algorithm is fast and insensitive to initial point. Theory analysis and numerical results illustrate that the smoothing approximation for the infinite SVM is feasible and effective.
{"title":"A smoothing approximation for L∞ SVM","authors":"Ruopeng Wang, Hongmin Xu, Hong Shi, Xu You","doi":"10.1109/ICNC.2012.6234775","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234775","url":null,"abstract":"In this paper, the infinite norm SVM is considered and a novel smoothing approximation function for Support Vector Machine is proposed in attempt to overcome some drawbacks of the former method which are complex, subtle, and sometimes difficult to implement. Firstly, we use Karush-Kuhn-Tucker complementary condition in optimization theory, and the unconstrained non-differentiable optimization model is built. Then the smooth approximation algorithm based on differentiable function is given. Finally, the paper trains the data sets with standard unconstraint optimization method. This algorithm is fast and insensitive to initial point. Theory analysis and numerical results illustrate that the smoothing approximation for the infinite SVM is feasible and effective.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"34 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":"127886354","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.2012.6234608
Zhe-Hao Liang, Wei Lu
It aims at technologically forecasting the serum luteinizing hormone(LH) peak value by means of the artificial neural network combined with the ultrasound in the examination of exciting the gonadotropin releasing hormone(GnRH). In the process, 71 girls of the sexual precocity are selected to take the conventional ultrasonic testing on the uterus and ovary. And then, the uterus size, the ovary size and the inner diameter of the biggest ovarian follicle in the 61 of those selected girls are set to be the input variable while the LH peak value the output variable. And BP neural network is in formation, and another 10 girls are used as testing targets. As a result, the linear regression is used as a method to calculate the real value and the BP network forecasting value, showing that the correlation coefficient of the linear regression is 0.9485 and the slope is 0.9280. In conclusion, the LH peak value in the examination of GnRH can be predicted by using the ultrasound combined with the BP neural network.
{"title":"Prognosis of the sexually-precocious girl's luteinizing hormone peak value with the neural network and ultrasonic","authors":"Zhe-Hao Liang, Wei Lu","doi":"10.1109/ICNC.2012.6234608","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234608","url":null,"abstract":"It aims at technologically forecasting the serum luteinizing hormone(LH) peak value by means of the artificial neural network combined with the ultrasound in the examination of exciting the gonadotropin releasing hormone(GnRH). In the process, 71 girls of the sexual precocity are selected to take the conventional ultrasonic testing on the uterus and ovary. And then, the uterus size, the ovary size and the inner diameter of the biggest ovarian follicle in the 61 of those selected girls are set to be the input variable while the LH peak value the output variable. And BP neural network is in formation, and another 10 girls are used as testing targets. As a result, the linear regression is used as a method to calculate the real value and the BP network forecasting value, showing that the correlation coefficient of the linear regression is 0.9485 and the slope is 0.9280. In conclusion, the LH peak value in the examination of GnRH can be predicted by using the ultrasound combined with the BP neural network.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"71 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":"127096134","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.2012.6234659
Hongbing Liu, Mingke Fang, Chang-an Wu
The classification error rate and the number of granules are two important objectives in granular computing. As two conflict objectives, optimizing them simultaneously is impossible. Evolutionary multi-objective granular computing classifiers are proposed to seek the tradeoff between the minimal classification error rate and the minimal number of granules. The individual is represented as the two-layer structure, the first layer is composed of the sequence of granule, and the second layer includes the beginning points, the end point, and the class labels of granules. Importance-based Pareto (IPareto) dominance is used to the comparison of two individuals. Crossover operation, union operation, and mutation operation designed specially for Granular Computing are performed the evolution process. Compared with Pareto front, IPareto front corresponded to more classifiers for two-class problems and multi-class problems.
{"title":"Evolutionary multi-objective granular computing classifiers","authors":"Hongbing Liu, Mingke Fang, Chang-an Wu","doi":"10.1109/ICNC.2012.6234659","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234659","url":null,"abstract":"The classification error rate and the number of granules are two important objectives in granular computing. As two conflict objectives, optimizing them simultaneously is impossible. Evolutionary multi-objective granular computing classifiers are proposed to seek the tradeoff between the minimal classification error rate and the minimal number of granules. The individual is represented as the two-layer structure, the first layer is composed of the sequence of granule, and the second layer includes the beginning points, the end point, and the class labels of granules. Importance-based Pareto (IPareto) dominance is used to the comparison of two individuals. Crossover operation, union operation, and mutation operation designed specially for Granular Computing are performed the evolution process. Compared with Pareto front, IPareto front corresponded to more classifiers for two-class problems and multi-class problems.","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":"114287835","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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