Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05.最新文献_第2页

Scored Pareto-MEC for multi-objective optimization 多目标优化的Pareto-MEC评分

Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. Pub Date : 2005-06-28 DOI: 10.1109/SMCIA.2005.1466956

Chengyi Sun, Wanzhen Wang, X.Z. Gao

{"title":"Scored Pareto-MEC for multi-objective optimization","authors":"Chengyi Sun, Wanzhen Wang, X.Z. Gao","doi":"10.1109/SMCIA.2005.1466956","DOIUrl":"https://doi.org/10.1109/SMCIA.2005.1466956","url":null,"abstract":"This paper proposes a new multi-objective optimization algorithm - scored Pareto mind evolutionary computation (SP-MEC), which introduces the theory of Pareto into mind evolutionary computation (MEC) for the multi-objective optimization. In our SP-MEC, the selection of individuals is based on their scores that include the Pareto dominance and density information among the individuals. The SP-MEC is compared with the VEGA, NSGA, SPEA, and Pareto-MEC on the basis of four different test problems: convexity, non-convexity, discreteness, as well as non-uniformity. Especially, both the Pareto-MEC and SPEA have shown promising performances in solving various optimization problems. On the test problems, SP-MEC outperforms all the four reference algorithms concerning three measures: the distance from trade-off front to Pareto-optimal front, the uniformity of solutions, and the spread of solutions. Impersonal termination criterion is used in SP-MEC and Pareto-MEC instead of the preset number of generations in other algorithms. SP-MEC has a higher computational efficiency than the VEGA, NSGA, and SPEA. Compared with another our algorithm, Pareto-MEC, the computational efficiency of SP-MEC is a little lower. However, the solution quality of SP-MEC is higher than that of the Pareto-MEC. Therefore, it can be concluded the SP-MEC is a powerful algorithm for multi-objective optimization.","PeriodicalId":283950,"journal":{"name":"Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130663563","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}

引用次数: 3

Predictive fuzzy logic controller for trajectory tracking of a mobile robot 移动机器人轨迹跟踪的预测模糊逻辑控制器

Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. Pub Date : 2005-06-28 DOI: 10.1109/SMCIA.2005.1466943

X. Jiang, Y. Motai, X. Zhu

引用次数: 38

Fault-tolerant networked control systems under varying load 变负荷下的容错网络控制系统

Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. Pub Date : 2005-06-28 DOI: 10.1109/SMCIA.2005.1466976

R. Daoud, H. Amer, H. Elsayed

引用次数: 18

Clonal selection algorithm in power filter optimization 电力滤波器优化中的克隆选择算法

Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. Pub Date : 2005-06-28 DOI: 10.1109/SMCIA.2005.1466959

Xiaolei Wang

引用次数: 21

Application-oriented document processing supported by knowledge database 基于知识库的面向应用的文档处理

Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. Pub Date : 2005-06-28 DOI: 10.1109/SMCIA.2005.1466978

Qiaolin Mao, Shilong Ma

引用次数: 1

Hierarchical two-population genetic algorithm 分层双种群遗传算法

Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. Pub Date : 2005-06-28 DOI: 10.1109/SMCIA.2005.1466954

J. Martikainen, S. Ovaska

引用次数: 16

Wireless intelligent temperature sensor for vehicle refrigerator 车载冰箱无线智能温度传感器

Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. Pub Date : 2005-06-28 DOI: 10.1109/SMCIA.2005.1466977

Q. Shan, D. Brown, Y. Liu

引用次数: 1

Reduction of the entries number of the training set for ANN through formal concept analysis and its application to solar energy systems 通过形式概念分析减少人工神经网络训练集的条目数及其在太阳能系统中的应用

Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. Pub Date : 2005-06-28 DOI: 10.1109/SMCIA.2005.1466965

Renato Vimieiro, Luis E. Zárate, E. M. Pereira, A. S. C. Diniz

引用次数: 0

Multiobjective route selection for car navigation system using genetic algorithm 基于遗传算法的汽车导航系统多目标路径选择

Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. Pub Date : 2005-06-28 DOI: 10.1109/SMCIA.2005.1466971

B. Chakraborty, Takeaki Maeda, Goutam Chakraborty

{"title":"Multiobjective route selection for car navigation system using genetic algorithm","authors":"B. Chakraborty, Takeaki Maeda, Goutam Chakraborty","doi":"10.1109/SMCIA.2005.1466971","DOIUrl":"https://doi.org/10.1109/SMCIA.2005.1466971","url":null,"abstract":"Route planning is an important problem for a car navigation system. Given a set of origin-destination pair, there could be many possible routes for a driver. Search for shortest route from one point to another on a weighted graph is a well known problem and has several solutions like Dijkstra algorithm, Bellman-Ford algorithm etc. But in case of car navigation systems the shortest path may not be the best one from the point of view of driver's satisfaction. So, for a practical car navigation system in dynamical environment, we need to specify multiple and separate good (near optimal) choices according to multiple criteria which make the search space too large to find out the solution in real time by deterministic algorithms. Genetic algorithms (GA) are now widely used to solve search problems with applications in practical routing and optimization problems. GA includes a variety of quasi optimal solutions, which can be obtained in a given time. In this work we propose a GA based algorithm to find out simultaneously several alternate routes depending on different criterion according to driver's choice such as shortest path by distance, path which contains minimum number of turns, path passing through mountains or by the side of a river etc. The proposed algorithm has been evaluated by simulation experiment using real road map compared to other existing GA based algorithms. It has been found that the proposed algorithm is quite efficient in finding alternate non overlapping routes with different characteristics.","PeriodicalId":283950,"journal":{"name":"Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05.","volume":"10 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126034727","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}

引用次数: 50

Prediction in dynamic system - a divide and conquer approach 动态系统的预测——分而治之方法

Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05. Pub Date : 2005-06-28 DOI: 10.1109/SMCIA.2005.1466972

Goutam Chakraborty, H. Watanabe, B. Chakraborty

{"title":"Prediction in dynamic system - a divide and conquer approach","authors":"Goutam Chakraborty, H. Watanabe, B. Chakraborty","doi":"10.1109/SMCIA.2005.1466972","DOIUrl":"https://doi.org/10.1109/SMCIA.2005.1466972","url":null,"abstract":"The aim of this work is to find a general framework for making decision from large and complex dynamic data, where all the influencing attributes are not known, and some of the available, apparently relevant, attributes could really be irrelevant. Some examples are: various diagnostic data from patient monitoring system, data from financial market, or the sales data of a big chain store for analyzing the buying pattern of customers. In these systems, many factors interact in a complex manner so that a complete analysis is often impossible, and conventional statistical methods for prediction also fail. In this work, we propose a framework for prediction in such problems using soft computing tools. We find partitions in the multivariate space so that data of same targeted forecast or decision are grouped there to the extent possible. This search is performed at different subspace level, i.e., a feature-subset selection. This part is accomplished by using rough set theory, after discretizing the original data. We conjecture that, if sufficient number of data with same decision falls in one of those subspace partition, any data in that partition would be predictable. A data can be member of more than one such subspace partition. In the next step, we train individual neural networks for each such subspace partition to learn the input-decision mapping using the original continuous valued data belonging to that subspace partition. For a new data, the ensemble of the trained neural network expert systems takes the decision. When applied to the prediction of stock-value, our model gave better results compared to other methods.","PeriodicalId":283950,"journal":{"name":"Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications, 2005. SMCia/05.","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125622777","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}

引用次数: 2