Ying Tan, Yuhui Shi, Carlos A Coello Coello's Advances in Swarm Intelligence: 5th International PDF

By Ying Tan, Yuhui Shi, Carlos A Coello Coello

ISBN-10: 331911896X

ISBN-13: 9783319118963

ISBN-10: 3319118978

ISBN-13: 9783319118970

This booklet and its significant other quantity, LNCS vol. 8794 and 8795 represent the complaints of the fifth foreign convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers offered have been rigorously reviewed and chosen from 198 submissions. The papers are prepared in 18 cohesive sections, three specified classes and one aggressive consultation masking all significant subject matters of swarm intelligence study and improvement akin to novel swarm-based seek equipment; novel optimization set of rules; particle swarm optimization; ant colony optimization for traveling salesman challenge; man made bee colony algorithms; synthetic immune process; evolutionary algorithms; neural networks and fuzzy equipment; hybrid equipment; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; category equipment; GPU-based equipment; scheduling and course making plans; instant sensor networks; strength process optimization; swarm intelligence in snapshot and video processing; functions of swarm intelligence to administration difficulties; swarm intelligence for real-world application.

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Additional info for Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II

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In addition, α and β are two parameters corresponding to the importance of the pheromone trail and heuristic information. When μR ( D) = μC ( D ) , the construction process stops, where R is the current solution constructed by an ant. Pheromone Update. After each ant has constructed its own solution, the pheromone of only edges along the path visited by the ant is updated as A Novel Rough Set Reduct Algorithm to Feature Selection Based on AFSA τij (t +1) = ρτij (t ) + q / Lmin 27 (3) While for other edges, the pheromone trails are updated according to the following equation.

Rahul et al. used multilayer perceptron neural networks (MLPNNs), radial basis function network (RBFN), competitive learning network (CL), learning vector quantization network(LVQ), combined neural networks (CNNs), probabilistic neural networks(PNNs), and recurrent neural networks (RNNs) for breast cancer diagnosis [14]. The artificial immune system with the GA in one hybrid algorithm which is the clonal selection algorithm was inspired from the clonal selection principle and affinity maturation of the human immune responses by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space.

Let X c represent the center n position in its visual scope. If Yi < Yc and f < δ , it denotes the center position has n higher food concentration and is not crowded. It moves a step toward the center position. Otherwise, it performs the preying behavior. The center position X c of m fishes is defined as  1,  X c (i ) =  0,  m m 2 k =1 m m X k (i ) ≤  2 k =1 X k (i ) ≥ i = 1, 2,3, , D (9) Preying Behavior. In the preying behavior, when the AF current state is X i , it needs to select a state Yj randomly in its visual scope.

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Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II by Ying Tan, Yuhui Shi, Carlos A Coello Coello

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