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 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 rigorously 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 next issues are coated during this quantity: research of swarm intelligence established 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 equipment, evolutionary programming and evolutionary games.
Read or Download Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part I PDF
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Extra info for Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part I
In general, the order of the average optimization result of all control strategies from high to low is: S3, S2, S1, S4. Fig. 5. Average evolutionary curve of f1 Fig. 6. Average evolutionary curve of f2 Fig. 7. Average evolutionary curve of f3 Fig. 8. Average evolutionary curve of f4 To further verify the importance of three operators in the evolutionary process of SFLA, the authors counted the average effective improved times of each operator among SO, GO and RO which simultaneously existed in the algorithm, the result was listed in Table 2, in which the values in brackets are their success ratio (the proportion of average effective numbers in the total numbers).
Liu, S. Zheng, and Y. Tan or fireworks for the selection of next generation fireworks’ location. (7). (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.
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. This paper deeply studies the diversity of SFLA by using the concept of swarm diversity presented in , and analyzes the impact of three operators of SFLA on the swarm diversity by simulation experiments and propose some guidance advices for improvement of the algorithm.