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 complaints of the 4th foreign convention on Swarm Intelligence, ICSI 2013, held in Harbin, China in June 2013. The 129 revised complete papers provided have been rigorously reviewed and chosen from 268 submissions. The papers are geared up in 22 cohesive sections protecting all significant subject matters of swarm intelligence study and advancements. the next issues are lined 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 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
Reference  proposed a new method which integrates a diversity control strategy into quantum-behaved particle swarm optimization (PSO) to guide the particle’s search and improve the capabilities of exploration. To overcome the premature convergence, exploitation ability of PSO was regulated through introducing fitness variance and position variance of the whole population in the evolutionary process to preserve the diversity of population in . In reference , the searching behavior of Y.
2303–2309. IEEE (2007) 14. : Fireworks algorithm for optimization. Advances in Swarm Intelligence pp. 355–364 (2010) 15. : Enhanced fireworks algirithm. In: IEEE International Conference on Evolutionary Computation. IEEE (submitted, 2013) 16. : Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. com Abstract. The diversity of population is an important indicator for measuring optimal performance of swarm intelligence algorithms. The effect of three operators of Shuffled Frog Leaping Algorithm (SFLA) on the diversity of population and the average optimization results were analyzed in this paper by means of the simulation experiments.
11) of original FWA don’t consider the fitness to select the firework location. Therefore, there are two methods to be provided to modify the selection operator. 1. Fitness Selection using the roulette Like the original FWA, the best of the set will be selected first. Then the others are selected base on fitness proportion using the roulette. So, the selection probability of every spark or firework must be calculated with the following formula: p(xi ) = ymax − f (xi ) (ymax − f (xi )) (12) i∈K Where ymax is the maximum value of the objective function among the set K which consist of the fireworks and sparks in the current generation.
Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part I by Tim Landgraf, Hai Nguyen, Stefan Forgo, Jan Schneider, Joseph Schröer, Christoph Krüger (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)