By Tim Landgraf, Hai Nguyen, Stefan Forgo, Jan Schneider, Joseph Schröer, Christoph Krüger (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)
This ebook and its significant other quantity, LNCS vols. 7928 and 7929 represent the complaints of the 4th foreign convention on Swarm Intelligence, ICSI 2013, held in Harbin, China in June 2013. The 129 revised complete papers offered have been conscientiously reviewed and chosen from 268 submissions. The papers are equipped in 22 cohesive sections overlaying all significant issues of swarm intelligence learn and advancements. the subsequent issues are coated during this quantity: research of swarm intelligence established algorithms, particle swarm optimization, purposes of particle swarm optimization algorithms, ant colony optimization algorithms, biogeography-based optimization algorithms, novel swarm-based seek equipment, bee colony algorithms, differential evolution, neural networks, fuzzy tools, evolutionary programming and evolutionary games.
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Extra resources for Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part I
Shuffled Frog Leaping Algorithm (SFLA) is one of biological evolutionary algorithms based on swarm intelligence presented by Eusuff and Lansey in 2003 . It has the characteristics such as simple concept, fewer parameters, fast calculation speed, powerful optimal performance and easy to realize. SFLA has been successfully applied to many fields such as water distribution network, function optimization, network optimization of product oil pipeline, combination optimization, image processing, multi-user detection and power system optimization.
Finally, reference  added the fuzzy controller and location hopping strategy to the algorithm for controlling the swarm diversity of PSO and improving its ability to jump out of local optimal solution. Shuffled Frog Leaping Algorithm (SFLA) is one of biological evolutionary algorithms based on swarm intelligence presented by Eusuff and Lansey in 2003 . It has the characteristics such as simple concept, fewer parameters, fast calculation speed, powerful optimal performance and easy to realize.
Currently, we are preparing experiments with live Sticklebacks and two robotic ﬁsh to investigate the swarm behavior with robotic ﬁsh exhibiting a diﬀerent morphology. References 1. : A novel method for investigating the collective behaviour of ﬁsh. Behav. Ecol. Sociobiol. 64(8), 1211–1218 (2010) 2. : Social integration of robots into groups of cockroaches to control self-organised choices. Science 318(5853), 1155–1158 (2007) 10 T. Landgraf et al. 3. : A biomimetic honeybee robot for the analysis of the honeybee dance communication system.