Download e-book for kindle: Advances in Swarm Intelligence: Second International by Li Nie, Liang Gao, Peigen Li, Xiaojuan Wang (auth.), Ying

By Li Nie, Liang Gao, Peigen Li, Xiaojuan Wang (auth.), Ying Tan, Yuhui Shi, Yi Chai, Guoyin Wang (eds.)

ISBN-10: 3642215246

ISBN-13: 9783642215247

The two-volume set (LNCS 6728 and 6729) constitutes the refereed court cases of the overseas convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers provided have been rigorously reviewed and chosen from 298 submissions. The papers are equipped in topical sections on theoretical research of swarm intelligence algorithms, particle swarm optimization, purposes of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, man made immune process, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy tools, and hybrid algorithms - for half I. themes addressed partially II are akin to multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent platforms, facts mining tools, computer studying tools, characteristic choice algorithms, trend reputation tools, clever regulate, different optimization algorithms and purposes, facts fusion and swarm intelligence, in addition to fish college seek - foundations and applications.

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Additional resources for Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part II

Example text

The results are listed in Table 1. R-Fi denotes the SRs obtained by GEP for single objective of Fi (i = 1,…,4), respectively. Ri denotes the SRs obtained by MOGEP for the objective of Fi (i = 1,…,4) simultaneously. Take the result on instance 1-1 for example. The solutions generated on instance 1-1 with the MOGEP-created SRs R1, R2, R3 and R4 are X1={5167, 1557, 3218, 385}, X2={5167, 2397, 1338, 368}, X3={5167, 2428, 1330, 385}, and X4={5167, 2292, 2601, 234}, respectively. The solution generated on instance 1-1 with the GEPcreated SR R-F1 is X-F1={5167, 2447, 3422, 675}.

4 Particle Mutation To improve the search diversity, our HMOPSO algorithm also use the mutation operation. For each dimension of each particle in the swarm, we first generate a random number rnd in [0, 1]. If rnd

Time Series Prediction Based on Gene Expression Programming. , Feng, L. ) WAIM 2004. LNCS, vol. 3129, pp. 55–64. Springer, Heidelberg (2004) 19. : Clustering without Prior Knowledge Based on Gene Expression Programming. In: 3rd International Conference on Natural Computation, pp. cn Abstract. The purpose of a multi-vehicle assignment problem is to allocate vehicles to complete various missions at different destinations, meanwhile it is required to satisfy all constrains and optimize overall criteria.

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Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part II by Li Nie, Liang Gao, Peigen Li, Xiaojuan Wang (auth.), Ying Tan, Yuhui Shi, Yi Chai, Guoyin Wang (eds.)


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