By Tim Landgraf, Hai Nguyen, Stefan Forgo, Jan Schneider, Joseph Schröer, Christoph Krüger (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)
This publication and its better half quantity, LNCS vols. 7928 and 7929 represent the lawsuits of the 4th overseas convention on Swarm Intelligence, ICSI 2013, held in Harbin, China in June 2013. The 129 revised complete papers awarded have been conscientiously reviewed and chosen from 268 submissions. The papers are prepared in 22 cohesive sections overlaying all significant subject matters of swarm intelligence examine and advancements. the next subject matters are coated during this quantity: research of swarm intelligence dependent algorithms, particle swarm optimization, functions 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 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
My Abstract. Artiﬁcial chemistry is a man-made system that is similar to a real chemical system. It represents a good starting point to simulate cell processes from the bio-chemistry level. In this article, an artiﬁcial chemistry system which strikes a balance among closeness to reality, fast simulation speed and high ﬂexibility is proposed. Preliminary results have shown that the model can simulate a general reversible reaction well. Keywords: artiﬁcial chemistry, cell simulation. 1 Introduction Among the techniques used in artiﬁcial life , there is the technique of simulating the bio-chemistry of life using artiﬁcial chemistry.
Cultural firework algorithm and its application for digital filters design. International Journal of Modelling, Identification and Control 14(4), 324–331 (2011) 5. : Iterative improvement of the multiplicative update nmf algorithm using nature-inspired optimization. In: 2011 Seventh International Conference on Natural Computation (ICNC), vol. 3, pp. 1668–1672. IEEE (2011) The Improvement on Controlling Exploration and Exploitation of Firework Algorithm 23 6. : Swarm intelligence for non-negative matrix factorization.
In order to improve the equations on The Improvement on Controlling Exploration and Exploitation of Firework Algorithm 15 calculating the number of firework sparks and the amplitude of firework explosion, we use the sequence number of fireworks to compute the two values. Therefore, a transfer function must be designed to map the sequence number of fireworks to function value which is used to better calculate the amplitude and spark number of firework explosion. (5): 1 f (x) = 1+ e(x−1)/a (5) Where a is a control parameter to change the shape of the above function.