2014 IEEE Symposium on Swarm Intelligence最新文献

A discontinuous recurrent neural network with predefined time convergence for solution of linear programming 求解线性规划的一种具有预定义时间收敛性的不连续递归神经网络

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-09 DOI: 10.1109/SIS.2014.7011799

J. Sánchez‐Torres, E. Sánchez, A. Loukianov

引用次数: 58

Improving artifact selection via agent migration in multi-population cultural algorithms 在多种群文化算法中通过智能体迁移改进人工制品选择

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011810

Felicitas Mokom, Ziad Kobti

{"title":"Improving artifact selection via agent migration in multi-population cultural algorithms","authors":"Felicitas Mokom, Ziad Kobti","doi":"10.1109/SIS.2014.7011810","DOIUrl":"https://doi.org/10.1109/SIS.2014.7011810","url":null,"abstract":"Multi-population cultural algorithms are cultural evolutionary frameworks involving multiple independently evolving subpopulations. Artifact selection involves the ability of agents to autonomously reason about selecting artifacts towards achieving their goals. In this study, agent migration between populations in a multi-population cultural algorithm is explored as an approach for augmenting artifact selection knowledge in social agents. Embedded in a social simulation model the multipopulation cultural algorithm consists of two subpopulations where agents in one subpopulation consistently outperform agents in the other due to the presence of knowledge about certain artifacts. Social networks connect agents within a subpopulation and agent knowledge can be altered by members of their network or the best performers of their subpopulation. The model investigates agent migration with novel artifact knowledge from the advanced subpopulation to the underperforming one. Child safety restraint selection is provided as an implemented case study. Results demonstrate the benefits of migration with a higher likelihood of an increase in agent performance when the social network is enabled. The study shows that culturally evolving agents can improve artifact selection knowledge in the absence of standard interventions as a result of migration.","PeriodicalId":380286,"journal":{"name":"2014 IEEE Symposium on Swarm Intelligence","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125095996","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: 5

Asynchronous particle swarm optimization with discrete crossover 离散交叉的异步粒子群优化

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011788

A. Engelbrecht

引用次数: 7

The hybrid swarm intelligence for S-system model-based genetic network 基于s系统模型的遗传网络混合群体智能

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011785

W. Yeh, Chia-Ling Huang

{"title":"The hybrid swarm intelligence for S-system model-based genetic network","authors":"W. Yeh, Chia-Ling Huang","doi":"10.1109/SIS.2014.7011785","DOIUrl":"https://doi.org/10.1109/SIS.2014.7011785","url":null,"abstract":"The importance of any inferences that can be taken from underlying genetic networks of observed time-series data of gene expression patterns should not be overlooked. They are one of the largest topics within bioinformatics. The S-system model is one good choice for analyzing such genetic networks due to the fact that it can capture various dynamics. One problem this model faces is the fact that the number of S-system parameters is in proportion with the square of the number of genes. This is also the reasoning as to why the S-system model tends to be used on smaller scales. Its parameters are optimized by hybrid soft computing. Furthermore, it also uses the problem decomposition strategy to deal with the vast amount of problems a system might face. First of all the original problem is split into several smaller parts, which are then separately solved by the SSO. Afterwards, all of these separate solutions are merged together and used to solve the original problem along with the ABC. This shows the effectiveness of the SSO in solving such sub problems. Lastly, the SSO also utilizes the hybrid soft computing system, which infers the possibility of having S-systems on a larger scale.","PeriodicalId":380286,"journal":{"name":"2014 IEEE Symposium on Swarm Intelligence","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129899328","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: 0

A fuzzy system for parameter adaptation in ant colony optimization 蚁群优化中参数自适应的模糊系统

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011780

Frumen Olivas, F. Valdez, O. Castillo

引用次数: 9

Repellent pheromones for effective swarm robot search in unknown environments 驱避信息素用于群体机器人在未知环境中的有效搜索

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011802

Filip Fossum, Jean-Marc Montanier, P. Haddow

引用次数: 25

Comparison of self-adaptive particle swarm optimizers 自适应粒子群优化算法的比较

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011775

E. V. Zyl, A. Engelbrecht

引用次数: 17

Communication-aware distributed PSO for dynamic robotic search 动态机器人搜索的通信感知分布式粒子群算法

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011777

L. Perreault, Mike P. Wittie, John W. Sheppard

引用次数: 4

Engineering optimization using interior search algorithm 利用内部搜索算法进行工程优化

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011771

A. Gandomi, D. Roke

引用次数: 28

Human-swarm hybrids outperform both humans and swarms solving digital Jigsaw puzzles 人类-蜂群混血儿在解决数字拼图游戏时的表现优于人类和蜂群

2014 IEEE Symposium on Swarm Intelligence Pub Date : 2014-12-01 DOI: 10.1109/SIS.2014.7011797

Daniel W. Palmer, M. Kirschenbaum, Eric Mustee, Jason Dengler

引用次数: 3