By Shangce Gao, Hongjian Chai, Beibei Chen, Gang Yang (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)
This ebook and its significant other quantity, LNCS vols. 7928 and 7929 represent the court cases of the 4th overseas convention on Swarm Intelligence, ICSI 2013, held in Harbin, China in June 2013. The 129 revised complete papers offered have been conscientiously reviewed and chosen from 268 submissions. The papers are geared up in 22 cohesive sections protecting all significant subject matters of swarm intelligence learn and advancements. the subjects lined during this quantity are: hybrid algorithms, swarm-robot and multi-agent structures, aid vector machines, info mining equipment, approach and knowledge safeguard, clever keep watch over, instant sensor community, scheduling and direction making plans, photo and video processing, and different applications.
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Additional resources for Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part II
5. The evolution of a network based on IBTM and the comparison with a real organism. (a) to (c) is the evolving process of IBTM to form a network connecting three active zones, and (d) is a network generated by the plasmodium of Physarum. The time unit is minute. A Physarum Network Evolution Model Based on IBTM 25 Table 1. 95 200 minute Mean 160 120 80 40 0 CELL Model IBTM Fig. 6. 52% by IBTM respectively. Hence, this conﬁrms that the eﬃciency and stability of CELL have a signiﬁcant improvement by the two optimization strategies.
If agent i satisﬁes C1 or C2 then 11. Disappear(); //“disappearance behavior” 12. Else 13. For each agent do 16. 2 Veriﬁcation with Biological Experiments Figure 5 shows the evolving process of an adaptive network based on IBTM. A time label is used to drive agents to transport towards the inside of internal grids. And we deploy three agents in parallel. As time goes on, a stable route comes out. The ﬁnal pattern of Fig. 5(c) is very similar to the biological pattern observed in a real organism as shown in Fig.
Neural Computing and Application 3, 515–521 (2012) 13. : Time series forecasting for dynamic environments the DyFor genetic program model. IEEE Transactions on Evolutionary Computation 11(4), 433–452 (2007) 14. : Long memory time series forecasting by using genetic programming. Genet. Program Evolvable Mach. 12, 429–456 (2011) 15. : GP-Based electricity price forecasting. , Giacobini, M. ) EuroGP 2011. LNCS, vol. 6621, pp. 37–48. Springer, Heidelberg (2011) 16. : Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp.