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Titlebook: Ant Colony Optimization and Swarm Intelligence; 5th International Wo Marco Dorigo,Luca Maria Gambardella,Thomas Stützle Conference proceedi

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發(fā)表于 2025-3-21 17:29:56 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Ant Colony Optimization and Swarm Intelligence
期刊簡稱5th International Wo
影響因子2023Marco Dorigo,Luca Maria Gambardella,Thomas Stützle
視頻videohttp://file.papertrans.cn/159/158178/158178.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Ant Colony Optimization and Swarm Intelligence; 5th International Wo Marco Dorigo,Luca Maria Gambardella,Thomas Stützle Conference proceedi
影響因子ANTS – The International Workshop on Ant Colony Optimization and Swarm Intelligence is now at its ?fth edition. The series started in 1998 with the - ganization of ANTS 1998. At that time the goal was to gather in a common meeting those researchers interested in ant colony optimization: more than 50 researchers from around the world joined for the ?rst time in Brussels, Belgium, to discuss ant colony optimization and swarm intelligence related research. A selectionofthebest paperspresentedatthe workshopwaspublished asa special issue of the Future Generation Computer Systems journal (Vol. 16, No. 8, 2000). Two years later, ANTS 2000, organized again in Brussels, attracted more than 70 participants. The 41 extended abstracts presented as talks or posters at the workshopwere collected in a booklet distributed to participants, and a selection of the best papers was published as a special section of the IEEE Transactions on Evolutionary Computation (Vol. 6, No. 4, 2002). After these ?rst two successful editions, it was decided to make of ANTS a seriesofbiannualeventswitho?cialworkshopproceedings.Thethirdandfourth editions were organized in September 2002 and September 2004, respectively
Pindex Conference proceedings 2006
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https://doi.org/10.1007/11839088Ant algorithm; algorithm; algorithms; intelligence; operations research; optimization; robot; algorithm ana
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978-3-540-38482-3Springer-Verlag Berlin Heidelberg 2006
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Eine blühende Psychiatrie in Gefahributions (RLDs). The advantage of our approach over the usual report pattern (average iterations to reach a predefined goal, success rates, and standard deviations) found in the current PSO literature is that it is possible to evaluate the performance of an algorithm on different application scenari
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