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2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)最新文献

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Stable flocking algorithm for multi-robot systems formation control 多机器人系统编队控制的稳定群集算法
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630997
Bin Lei, Wenfeng Li, Fan Zhang
{"title":"Stable flocking algorithm for multi-robot systems formation control","authors":"Bin Lei, Wenfeng Li, Fan Zhang","doi":"10.1109/CEC.2008.4630997","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630997","url":null,"abstract":"The problem of multiple robots system formation using a distributed control method is studied in this paper. The main idea of this paper is that uses swarm flocking control algorithm to implement the ldquobiodsrdquo model of Reynolds among multi-robots. With the help of graph theory, we propose a provably-stable flocking control law, which ensures that the internal group formation is stabilized to a desired shape, while all the robotspsila velocities and directions converge to the same. Player/stage simulation results show that the proposed method can be efficiently applied to multiple robots formation control. With the characteristic of player/stage, the algorithm in this paper can be implemented on the real robots with so few or no changes.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127626590","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: 4
Two Level Parallel Grammatical Evolution 两个层次的平行语法演变
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631129
P. Osmera, O. Popelka, P. Pivoñka
{"title":"Two Level Parallel Grammatical Evolution","authors":"P. Osmera, O. Popelka, P. Pivoñka","doi":"10.1109/CEC.2008.4631129","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631129","url":null,"abstract":"This paper describes a two level parallel grammatical evolution (TLPGE) that can evolve complete programs using a variable length linear genome to govern the mapping of a Backus Naur Form grammar definition. To increase the efficiency of grammatical evolution (GE) the influence of backward processing was tested and a second level with differential evolution was added. The significance of backward coding (BC) and the comparison with standard coding of GEs is presented. The new method is based on parallel grammatical evolution (PGE) with a backward processing algorithm, which is further extended with a differential evolution algorithm. Thus a two-level optimization method was formed in attempt to take advantage of the benefits of both original methods and avoid their difficulties. Both methods used are discussed and the architecture of their combination is described. Also application is discussed and results on a real-word application are described.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127943921","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: 4
A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution 一种重新开始的单变量分布估计算法:混合高斯和lsamvy概率分布下的抽样
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631330
Yu Wang, Bin Li
{"title":"A restart univariate estimation of distribution algorithm: sampling under mixed Gaussian and Lévy probability distribution","authors":"Yu Wang, Bin Li","doi":"10.1109/CEC.2008.4631330","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631330","url":null,"abstract":"A univariate EDA denoted as ldquoLSEDA-glrdquo for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, standard deviation control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ldquocontinuous univariate marginal distribution algorithmrdquo UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CECpsila2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CECpsila2008 special session.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131709242","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: 61
Co-evolution model of networks and strategy in a 2 × 2 game emerges cooperation 2 × 2博弈中网络与策略的协同进化模型出现了合作
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630785
J. Tanimoto
{"title":"Co-evolution model of networks and strategy in a 2 × 2 game emerges cooperation","authors":"J. Tanimoto","doi":"10.1109/CEC.2008.4630785","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630785","url":null,"abstract":"A 2 times 2 game model implemented by co-evolution of both networks and strategies is established. Several numerical experiments considering various 2 times 2 game classes, including Prisonerpsilas Dilemma (PD), Chicken, Leader, and Hero, reveal that the proposed co-evolution mechanism can solve dilemmas in the PD game class. The result of solving a dilemma is the development of mutual-cooperation reciprocity (R reciprocity), which arises through the emergence of several cooperative hub agents, which have many links in a heterogeneous and assortative social network. However, the co-evolution mechanism seems counterproductive in case of the Leader and Hero game classes, where alternating reciprocity (ST reciprocity) is more demanding. It is also suggested that the assortative and cluster coefficients of a network affect the emergence of cooperation for R reciprocity.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"681 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125354129","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: 4
An intelligent multi-colony multi-objective ant colony optimization (ACO) for the 0–1 knapsack problem 针对0-1背包问题的智能多群体多目标蚁群优化
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630948
S. K. Chaharsooghi, Amir Hosein Meimand Kermani
{"title":"An intelligent multi-colony multi-objective ant colony optimization (ACO) for the 0–1 knapsack problem","authors":"S. K. Chaharsooghi, Amir Hosein Meimand Kermani","doi":"10.1109/CEC.2008.4630948","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630948","url":null,"abstract":"The knapsack problem is a famous optimization problem. Even the single objective case has been proven to be NP-hard the multi-objective is harder than the single objective case. This paper presents the modified ant colony optimization (ACO) algorithm for solving knapsack multi-objective problem to achieve the best layer of non-dominated solution. We also proposed a new pheromone updating rule for multi-objective case which can increase the learning of algorithm and consequently increase effectiveness. Finally, the computational result of proposed algorithm is compared with the NSGA II which outperforms most of the multi-objective ant colony optimization algorithm which are reviewed in this paper.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126749027","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: 11
Evolutionary lossless compression with GP-ZIP 进化无损压缩与GP-ZIP
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1145/1389095.1389333
A. Kattan, R. Poli
{"title":"Evolutionary lossless compression with GP-ZIP","authors":"A. Kattan, R. Poli","doi":"10.1145/1389095.1389333","DOIUrl":"https://doi.org/10.1145/1389095.1389333","url":null,"abstract":"In this paper we propose a new approach for applying genetic programming to lossless data compression based on combining well-known lossless compression algorithms. The file to be compressed is divided into chunks of a predefined length, and GP is asked to find the best possible compression algorithm for each chunk in such a way to minimise the total length of the compressed file. This technique is referred to as ldquoGP-ziprdquo: The compression algorithms available to GP-zip (its function set) are: arithmetic coding (AC), Lempel-Ziv-Welch (LZW), unbounded prediction by partial matching (PPMD), run length encoding (RLE), and Boolean minimization. In addition, two transformation techniques are available: Burrows-Wheeler transformation (BWT) and move to front (MTF). In experimentation with this technique, we show that when the file to be compressed is composed of heterogeneous data fragments (as is the case, for example, in archive files), GP-zip is capable of achieving compression ratios that are superior to those obtained with well-known compression algorithms.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123189932","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: 22
Development of software effort and schedule estimation models using Soft Computing Techniques 利用软计算技术开发软件工作量和进度估计模型
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630961
A. Sheta, D. Rine, A. Ayesh
{"title":"Development of software effort and schedule estimation models using Soft Computing Techniques","authors":"A. Sheta, D. Rine, A. Ayesh","doi":"10.1109/CEC.2008.4630961","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630961","url":null,"abstract":"Accurate estimation of the software effort and schedule affects the budget computation. Bidding for contracts depends mainly on the estimated cost. Inaccurate estimates will lead to failure of making a profit, increased probability of project incompletion and delay of the project delivery date. In this paper, we explore the use of Soft Computing Techniques to build a suitable model structure to utilize improved estimations of software effort for NASA software projects. In doing so, we plan to use Particle Swarm Optimization (PSO) to tune the parameters of the famous COnstructive COst MOdel (COCOMO). We plan also to explore the advantages of Fuzzy Logic to build a set of linear models over the domain of possible software Line Of Code (LOC). The performance of the developed model was evaluated using NASA software projects data set [1]. A comparison between COCOMO tuned-PSO, Fuzzy Logic (FL), Halstead, Walston-Felix, Bailey-Basili and Doty models were provided.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126259068","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: 74
The “natura non facit saltus” principle in Memetic computing 模因计算中的“自然非事实状态”原则
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631325
V. Tirronen, Ferrante Neri, K. Majava, T. Kärkkäinen
{"title":"The “natura non facit saltus” principle in Memetic computing","authors":"V. Tirronen, Ferrante Neri, K. Majava, T. Kärkkäinen","doi":"10.1109/CEC.2008.4631325","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631325","url":null,"abstract":"This paper proposes the employment of continuous probability distributions instead of step functions for adaptive coordination of the local search in fitness diversity based memetic algorithms. Two probability distributions are considered in this study: the beta and exponential distributions. These probability distributions have been tested within two memetic frameworks present in literature. Numerical results show that employment of the probability distributions can be beneficial and improve performance of the original memetic algorithms on a set of test functions without varying the balance between the evolutionary and local search components.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122540509","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
Issues of grid-cluster retrievals in swarm-based clustering 基于群聚类的网格-聚类检索问题
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4630845
Swee Chuan Tan, K. Ting, S. Teng
{"title":"Issues of grid-cluster retrievals in swarm-based clustering","authors":"Swee Chuan Tan, K. Ting, S. Teng","doi":"10.1109/CEC.2008.4630845","DOIUrl":"https://doi.org/10.1109/CEC.2008.4630845","url":null,"abstract":"One common approach in swarm-based clustering is to use agents to create a set of clusters on a two-dimensional grid, and then use an existing clustering method to retrieve the clusters on the grid. The second step, which we call grid-cluster retrieval, is an essential step to obtain an explicit partitioning of data. In this study, we highlight the issues in grid-cluster retrievals commonly neglected by researchers, and demonstrate the non-trivial difficulties involved. To tackle the issues, we then evaluate three methods: K-means, hierarchical clustering (Weighted Single-link) and density-based clustering (DBScan). Among the three methods, DBScan is the only method which has not been previously used for grid-cluster retrievals, yet it is shown to be the most suitable method in terms of effectiveness and efficiency.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126576929","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: 1
A novel approach to distributed routing by super-AntNet 一种基于超级蚁网的分布式路由新方法
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI: 10.1109/CEC.2008.4631084
S. S. Aman, M. Akbarzadeh-Totonchi, Mahmoud Naghibzadeh
{"title":"A novel approach to distributed routing by super-AntNet","authors":"S. S. Aman, M. Akbarzadeh-Totonchi, Mahmoud Naghibzadeh","doi":"10.1109/CEC.2008.4631084","DOIUrl":"https://doi.org/10.1109/CEC.2008.4631084","url":null,"abstract":"Various forms of swarm intelligence are inspired by social behavior of insects that live collectively. AntNet is a form of such social algorithms, but it has a scalability problem with growing network size. If every node sends only one ant to each destination node and there are N nodes in the network, the total number of ants that are sent is N(N-1). In addition with increasing overhead for large networks, most of the ants are often lost for distant destinations. Furthermore, due to long travel times, ants that do arrive may carry outdated information. In this paper, a novel hierarchical algorithm is proposed to resolve this scalability problem of AntNet. The proposed Super-AntNet divides a large scale network into several small networks that are chosen based their internal traffic patterns. A separate ant colony is then assigned to each of these networks. A Super-Ant Colony is then responsible to coordinate data routing among the colonies. Performance of Super-AntNet is compared with those of standard AntNet as well as two other conventional routing algorithms such as Distance Vector (DV) and Link State (LS) in terms of end-to-end delay, throughput, packet loss ratio, increased overhead, as well as jitter. Application to a 16-node network indicates the superiority of the proposed algorithm.","PeriodicalId":328803,"journal":{"name":"2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126579566","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: 6
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