By Shangce Gao, Hongjian Chai, Beibei Chen, Gang Yang (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)
This e-book and its better half quantity, LNCS vols. 7928 and 7929 represent the complaints of the 4th overseas 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 equipped in 22 cohesive sections overlaying all significant issues of swarm intelligence study and advancements. the themes coated during this quantity are: hybrid algorithms, swarm-robot and multi-agent structures, aid vector machines, information mining equipment, method and data protection, clever keep an eye on, instant sensor community, scheduling and direction making plans, photograph and video processing, and different applications.
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Additional info for Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part II
The Cultural Algorithms are based on some theories which try to model cultural as an inheritance process operating at two levels: a Micro-evolutionary level, which consists of the genetic material that an oﬀspring inherits from its parent, and a Macro-evolutionary level, which is the knowledge acquired by the individuals through generations. This knowledge, once encoded and stored, it serves to guide the behavior of the individuals that belong to a population. Considering that evolution can be seen like an optimization process, Reynolds developed a computational model of cultural evolution that can have applications in optimization [4, 10].
CELL continually moves internal grids to active zones based on the bubble transportation, until one route connecting active zones appears. This ﬁnal route approximates an eﬃcient network produced by the plasmodium of Physarum. However, with the increment of active zones, an agent often transports within active zones, which restricts the evolving eﬃciency of CELL. Meanwhile, the mechanism of single agent’s transportation also restricts the eﬃciency of evolution. With those observations in mind, this paper proposes an improved model IBTM in order to improve the eﬃciency of CELL.
Fig. 6 shows the mean and standard value of evolving time based on the data in Table 1. 32m (d) Fig. 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.