By Shangce Gao, Hongjian Chai, Beibei Chen, Gang Yang (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)
This publication and its significant other 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 geared up in 22 cohesive sections protecting all significant issues of swarm intelligence study and advancements. the subjects lined during this quantity are: hybrid algorithms, swarm-robot and multi-agent structures, aid vector machines, info mining tools, approach and knowledge safety, clever regulate, instant sensor community, scheduling and course making plans, photo and video processing, and different applications.
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In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. Perth (1995) 17. : The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002) 18. : The fully informed particle swarm: Simpler, maybe better. IEEE Transactions on Evolutionary Computation 8, 204–210 (2004) 19. : A hierarchical particle swarm optimizer and its adaptive variant. IEEE Transactions on Systems, Man and Cybernetics - Part B 3(6), 1272–1282 (2005) 20.
Pso-based multiobjective optimization with dynamic population size and adaptive local archives. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38(5), 1270–1293 (2008) 13. : Convergence analysis and improvements of quantum-behaved particle swarm optimization. Information Sciences 193, 81–103 (2012) 14. : Chaos embedded particle swarm optimization algorithms. Chaos, Solitons & Fractals 40(4), 1715–1734 (2009) 15. : A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation 8(3), 225–239 (2004) 16.
Liu et al. based on feedback regulations between the internal protoplasmic ﬂow and the thickness of each tube. Adamatzky [10,11] has utilized an Oregonator model to simulate the growth and foraging behavior of plasmodium of Physarum. Jones [6,12] has illustrated the evolving process of Physarum-like transport networks through a bottom-up multi-agent based system. However, the aforementioned models are unable to further explain the relationship between the process of network formation and the movement of amoeba, which plays a central role in Physarum computing .
Advances in Swarm Intelligence: 4th International Conference, ICSI 2013, Harbin, China, June 12-15, 2013, Proceedings, Part II by Shangce Gao, Hongjian Chai, Beibei Chen, Gang Yang (auth.), Ying Tan, Yuhui Shi, Hongwei Mo (eds.)