Advances in Swarm Intelligence: First International Conference, ICSI 2010, Beijing, China, June 12-15, 2010, ProceedingsYing Tan, Yuhui Shi, KAY CHEN TAN This book and its companion volume, LNCS vols. 6145 and 6146, constitute the proceedings of the International Conference on Swarm Intelligence (ICSI 2010) held in Beijing, the capital of China, during June 12-15, 2010. ICSI 2010 was the ?rst gathering in the world for researchers working on all aspects of swarm intelligence, and providedan academic forum for the participants to disseminate theirnewresearch?ndingsanddiscussemergingareasofresearch.Italsocreated a stimulating environment for the participants to interact and exchange inf- mation on future challenges and opportunities of swarm intelligence research. ICSI 2010 received 394 submissions from about 1241 authors in 22 countries and regions (Australia, Belgium, Brazil, Canada, China, Cyprus, Hong Kong, Hungary, India, Islamic Republic of Iran, Japan, Jordan, Republic of Korea, Malaysia, Mexico, Norway, Pakistan, South Africa, Chinese Taiwan, UK, USA, Vietnam) across six continents (Asia, Europe, North America, South America, Africa, and Oceania). Each submission was reviewed by at least three reviewers. Based on rigorous reviews by the Program Committee members and reviewers, 185 high-quality papers were selected for publication in the proceedings with the acceptance rate of 46.9%. The papers are organized in 25 cohesive sections covering all major topics of swarm intelligence research and development. |
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according accuracy activities agent algorithm analysis applied approach average better calculate characteristics China classification clustering combination compared complex Computer condition consider cost data set defined denoted detection determine diagnosis distance distribution dynamic effect Engineering equation error estimation experiment fault follows formula function fuzzy given IEEE improved initial input Intelligence iteration Journal layer learning linear matrix maximum mean measure method module neural network nodes normal objective obtain operation optimization output parameters particle Pattern performance position prediction presented probability problem production programming proposed References regional relation represents respectively robot sample Science selection shown shows signal simulation solution solve space speed standard step strategy structure swarm Table Technology theory tion University variables vector weight