Advances in Swarm Intelligence: 4th International by Tim Landgraf, Hai Nguyen, Stefan Forgo, Jan Schneider,

By Tim Landgraf, Hai Nguyen, Stefan Forgo, Jan Schneider, Joseph Schröer, Christoph Krüger (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)

This booklet and its better half quantity, LNCS vols. 7928 and 7929 represent the court cases of the 4th foreign convention on Swarm Intelligence, ICSI 2013, held in Harbin, China in June 2013. The 129 revised complete papers awarded have been rigorously reviewed and chosen from 268 submissions. The papers are prepared in 22 cohesive sections protecting all significant themes of swarm intelligence examine and advancements. the subsequent themes are lined 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 tools, bee colony algorithms, differential evolution, neural networks, fuzzy equipment, evolutionary programming and evolutionary games.

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Additional resources for Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part I

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Zheng, and Y. Tan Table 1. Statistical Results of Mean, Std and Best of Benchmark Functions in 10 Dimension FES No. 27E+03 IFWABS FES No. 47E+0 The Improvement on Controlling Exploration and Exploitation of Firework Algorithm 21 Table 2. Statistical Results of Mean, Std and Best of Benchmark Functions in 30 Dimension FES No. 70E+03 IFWABS FES No. 25E+01 J. Liu, S. Zheng, and Y. 54 0 200 400 600 800 1000 Number of Function Iteration Fig. 6. Convergence cures on the benchmark functions Acknowledgements.

IEEE, Taipei (2010) 4. : Male displays adjusted to female’s response. Nature 415(6869), 279–280 (2002) 5. : Real time face and object tracking as a component of a perceptual user interface. In: Proc. of the Fourth IEEE Workshop on Applications of Computer Vision (WACV), pp. 214–219. IEEE, Princeton (1998) 6. : Real-Time Feedback-Controlled Robotic Fish for Behavioral Experiments With Fish Schools. Proc. R. cn Abstract. Firework algorithm (FWA) is a new Swarm Intelligence (SI) based optimization technique, which presents a different search manner and simulates the explosion of fireworks to search the optimal solution of problem.

1. 5 the amplitude of explosion J. Liu, S. Zheng, and Y. Tan the amplitude of explosion the amplitude of explosion the amplitude of explosion 14 the 4th firework 120 100 80 60 40 20 0 0 200 400 600 800 1000 800 1000 iteration the 8th firework 120 100 80 60 40 20 0 0 200 400 600 iteration Fig. 2. 2. 2 is the amplitude of explosion generated by eight fireworks with the FWA iteration. (3) provided that every iteration of FWA has only eight fireworks. 1, different fireworks in the different iteration have no regularity to gain the number of sparks.

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