Pub Date : 2012-05-29DOI: 10.1109/ICNC.2012.6234747
Mou Lian-Ming
The Generalized Traveling Salesman Problem (GTSP) extends the classical Traveling Salesman Problem (TSP) and has many interesting applications. In this paper we propose a Continuous Selective Generalized Traveling Salesman Problem (CSGTSP), and the existing GTSP is only a special case of the CSGTSP. To solving it effectively, we extend the ant colony system method from TSP to CSGTSP. Meanwhile, to speed up the convergence and improve the quality of solution, a constrained local searching technique is introduced into this method according to the characteristic of the CSGTSP. Experimental results on numerous TSPLIB instances show that the proposed method can deal with the CSGTSP fairly well, and the developed local searching technique is significantly effective.
{"title":"The continuous selective generalized traveling salesman problem: An efficient ant colony system","authors":"Mou Lian-Ming","doi":"10.1109/ICNC.2012.6234747","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234747","url":null,"abstract":"The Generalized Traveling Salesman Problem (GTSP) extends the classical Traveling Salesman Problem (TSP) and has many interesting applications. In this paper we propose a Continuous Selective Generalized Traveling Salesman Problem (CSGTSP), and the existing GTSP is only a special case of the CSGTSP. To solving it effectively, we extend the ant colony system method from TSP to CSGTSP. Meanwhile, to speed up the convergence and improve the quality of solution, a constrained local searching technique is introduced into this method according to the characteristic of the CSGTSP. Experimental results on numerous TSPLIB instances show that the proposed method can deal with the CSGTSP fairly well, and the developed local searching technique is significantly effective.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"31 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":"114447310","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.2012.6234698
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.2012.6234576
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.2012.6234582
Wuxi Shi, Yingxin Ma, Yuchan Chen, Ziguang Guo
An adaptive neural network control scheme is presented for a class of nonlinear discrete-time systems. The unknown nonlinear plants are represented by an equivalent model composed of a simple linear submodel plus a nonlinear submodel around operating points, and a simple linear controller is designed based on the linearization of the nonlinear system, a compensation term, which is implemented with a two-layer recurrent neural network during every sampling period, is introduced to control nonlinear systems, the network weight adaptation law is derived by using Lyapunov theory. The proposed design scheme guarantees that all the signals in closed-loop system are bounded, and the filtering tracking error converges to a small neighborhood of the origin.
{"title":"Adaptive neural network control for a class of nonlinear discrete system","authors":"Wuxi Shi, Yingxin Ma, Yuchan Chen, Ziguang Guo","doi":"10.1109/ICNC.2012.6234582","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234582","url":null,"abstract":"An adaptive neural network control scheme is presented for a class of nonlinear discrete-time systems. The unknown nonlinear plants are represented by an equivalent model composed of a simple linear submodel plus a nonlinear submodel around operating points, and a simple linear controller is designed based on the linearization of the nonlinear system, a compensation term, which is implemented with a two-layer recurrent neural network during every sampling period, is introduced to control nonlinear systems, the network weight adaptation law is derived by using Lyapunov theory. The proposed design scheme guarantees that all the signals in closed-loop system are bounded, and the filtering tracking error converges to a small neighborhood of the origin.","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":"121094026","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.2012.6234765
Zhuhong Zhang, Min Liao, Lei Wang
This work investigates one multi-objective immune genetic algorithm to solve dynamic constrained single-objective multimodal optimization problems in terms of the concept of constraint-dominance and biological immune inspirations. The algorithm assumes searching multiple global optimal solutions along diverse searching directions, by means of the environmental detection and two evolving subpopulations. It exploits various kinds of promising regions through executing the periodical suppression mechanism and periodically adjusting the mutation magnitude. The sufficient diversity of population can be maintained relying upon a dynamic suppression index, and meanwhile the high-quality solutions can be found rapidly during the process of solution search. Comparative experiments show that the proposed approach can not only outperform the compared algorithms, but also rapidly acquire the global optima in each environment for each test problem, and thus it is a competitive optimizer.
{"title":"Multi-objective immune genetic algorithm solving dynamic single-objective multimodal constrained optimization","authors":"Zhuhong Zhang, Min Liao, Lei Wang","doi":"10.1109/ICNC.2012.6234765","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234765","url":null,"abstract":"This work investigates one multi-objective immune genetic algorithm to solve dynamic constrained single-objective multimodal optimization problems in terms of the concept of constraint-dominance and biological immune inspirations. The algorithm assumes searching multiple global optimal solutions along diverse searching directions, by means of the environmental detection and two evolving subpopulations. It exploits various kinds of promising regions through executing the periodical suppression mechanism and periodically adjusting the mutation magnitude. The sufficient diversity of population can be maintained relying upon a dynamic suppression index, and meanwhile the high-quality solutions can be found rapidly during the process of solution search. Comparative experiments show that the proposed approach can not only outperform the compared algorithms, but also rapidly acquire the global optima in each environment for each test problem, and thus it is a competitive optimizer.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"1 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":"126085865","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.2012.6234564
Li-qian Zhou, Zuguo Yu, Guo-Sheng Han, Guang-ming Zhou, De-Sheng Wang
There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. Among them, CVTree method, feature frequency profiles method and dynamical language approach were used to investigate the whole-proteome phylogeny of large dsDNA viruses. Using the data set of large dsDNA viruses from Gao and Qi (BMC Evol. Biol. 2007), the phylogenetic results based on the CVTree method and the dynamical language approach were compared in Yu et al. (BMC Evol. Biol. 2010). In this paper, we first apply dynamical language approach to the data set of large dsDNA viruses from Wu et al. (Proc. Natl. Acad. Sci. USA 2009) and compare our phylogenetic results with those based on the feature frequency profiles method. Then we construct the whole-proteome phylogeny of the larger dataset combining the above two data sets. According to the report of The International Committee on the Taxonomy of Viruses (ICTV), the trees from our analyses are in good agreement to the latest classification of large dsDNA viruses.
人们对使用全基因组数据进行系统发育分析的无比对方法越来越感兴趣。其中，利用CVTree方法、特征频率谱方法和动态语言方法对大型dsDNA病毒的全蛋白质组系统发育进行了研究。利用高和齐(BMC Evol.)的大型dsDNA病毒数据集。Yu et al. (BMC evolution . 2007)对基于CVTree方法和动态语言方法的系统发育结果进行了比较。医学杂志。2010)。在本文中，我们首先将动态语言方法应用于Wu等人(Proc. Natl)的大型dsDNA病毒数据集。学会科学。(美国，2009)，并将我们的系统发育结果与基于特征频率谱方法的结果进行比较。然后结合以上两个数据集构建更大数据集的全蛋白质组系统发育。根据国际病毒分类委员会(International Committee on the Taxonomy of Viruses, ICTV)的报告，我们的分析树与大型dsDNA病毒的最新分类非常一致。
{"title":"Some comparison on whole-proteome phylogeny of large dsDNA viruses based on dynamical language approach and feature frequency profiles method","authors":"Li-qian Zhou, Zuguo Yu, Guo-Sheng Han, Guang-ming Zhou, De-Sheng Wang","doi":"10.1109/ICNC.2012.6234564","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234564","url":null,"abstract":"There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. Among them, CVTree method, feature frequency profiles method and dynamical language approach were used to investigate the whole-proteome phylogeny of large dsDNA viruses. Using the data set of large dsDNA viruses from Gao and Qi (BMC Evol. Biol. 2007), the phylogenetic results based on the CVTree method and the dynamical language approach were compared in Yu et al. (BMC Evol. Biol. 2010). In this paper, we first apply dynamical language approach to the data set of large dsDNA viruses from Wu et al. (Proc. Natl. Acad. Sci. USA 2009) and compare our phylogenetic results with those based on the feature frequency profiles method. Then we construct the whole-proteome phylogeny of the larger dataset combining the above two data sets. According to the report of The International Committee on the Taxonomy of Viruses (ICTV), the trees from our analyses are in good agreement to the latest classification of large dsDNA viruses.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"61 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":"114077153","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.2012.6234546
Wudai Liao, Xingfeng Wang, Yuyu Yang, Junyan Wang
In this paper, we introduce a kind of method for solving least mean square problems based on the gradient neural network, including the network model construction, quantitative analysis of the network global convergence and the network convergence rate about the different activation functions. MATLAB simulation results and theoretical analysis results are accordingly consistent, which further confirm the method based on Hopfield neural network has a good effect on solving the least mean square problems.
{"title":"Convergence and robustness analysis of disturbed gradient neural network for solving LMS problem","authors":"Wudai Liao, Xingfeng Wang, Yuyu Yang, Junyan Wang","doi":"10.1109/ICNC.2012.6234546","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234546","url":null,"abstract":"In this paper, we introduce a kind of method for solving least mean square problems based on the gradient neural network, including the network model construction, quantitative analysis of the network global convergence and the network convergence rate about the different activation functions. MATLAB simulation results and theoretical analysis results are accordingly consistent, which further confirm the method based on Hopfield neural network has a good effect on solving the least mean square problems.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"68 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":"127632691","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.2012.6234561
Shuai Wang, Xiao Lei, Xiaomin Huang
This paper proposes a method using multi-objective particle swarm optimization (MOPSO) algorithm to solve the multi-objective optimal dispatch problem of reservoir flood control, which take minimum value of the highest water level before dam, minimum value of the releasing peak discharge, and water level after flood season very close to flood control level as the objective functions. By using the archiving technique, crowding distance sorting algorithm and mutation technique to improve the algorithm convergence speed and accuracy and enable the Pareto solution set to converge to optimal front promptly and distribute evenly. The algorithm is applied to optimize the dispatch of the Yuecheng reservoir in upper Zhanghe River of the Haihe basin for typical floods occurred in history and the relative relations between dispatching objectives are analyzed. The result indicates that a lot of noninferior dispatch schemes can be generated in a short time, which can provide scientific basis for the decision-maker to make optimal operation and evaluation decision.
{"title":"Multi-objective optimization of reservoir flood dispatch based on MOPSO algorithm","authors":"Shuai Wang, Xiao Lei, Xiaomin Huang","doi":"10.1109/ICNC.2012.6234561","DOIUrl":"https://doi.org/10.1109/ICNC.2012.6234561","url":null,"abstract":"This paper proposes a method using multi-objective particle swarm optimization (MOPSO) algorithm to solve the multi-objective optimal dispatch problem of reservoir flood control, which take minimum value of the highest water level before dam, minimum value of the releasing peak discharge, and water level after flood season very close to flood control level as the objective functions. By using the archiving technique, crowding distance sorting algorithm and mutation technique to improve the algorithm convergence speed and accuracy and enable the Pareto solution set to converge to optimal front promptly and distribute evenly. The algorithm is applied to optimize the dispatch of the Yuecheng reservoir in upper Zhanghe River of the Haihe basin for typical floods occurred in history and the relative relations between dispatching objectives are analyzed. The result indicates that a lot of noninferior dispatch schemes can be generated in a short time, which can provide scientific basis for the decision-maker to make optimal operation and evaluation decision.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"1 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":"129250891","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：