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 significant other 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 geared up in 22 cohesive sections masking all significant issues of swarm intelligence learn and advancements. the next 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 equipment, bee colony algorithms, differential evolution, neural networks, fuzzy tools, 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
Table 1. 3 L. Wang and Y. Gong Experiments Results Each experiment for every test function had been repeated 30 times. Diversity in each iteration and the average of optimization results were recorded and drew as evolutionary curves to compare. All the experiments demonstrate similar results. 4. The result shows that the highest diversity is presented when the algorithm is without GO (S3) of every function while the worst is gained when the algorithm is without RO (S4). The order of the diversity of all control strategies from high to low is: S3, S2, S1, S4.
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. Since it was proposed, fireworks algorithm has shown its significance and superiority in dealing with the optimization problems. However, the calculation of number of explosion spark and amplitude of firework explosion of FWA should dynamically control the exploration and exploitation of searching space with iteration.
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.