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By De-Shuang Huang, Donald C. Wunsch, Daniel S. Levine, Kang-Hyun Jo

This booklet - along side the 2 volumes LNCS 5226 and LNAI 5227 - constitutes the refereed complaints of the Fourth overseas convention on clever Computing, ICIC 2008, held in Shanghai, China in September 2008. The clever computing know-how features a variety of suggestions corresponding to man made intelligence, perceptual and development reputation, evolutionary and adaptive computing, informatics theories and functions, computational neuroscience and bioscience, gentle computing, case established and restricted reasoning, brokers, networking and computing device supported co-operative operating, human machine interface concerns. ICIC subject unifies the image of up to date clever computing options as an necessary idea that highlights the traits in complicated computational intelligence and bridges theoretical examine with purposes.

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Extra resources for Advanced Intelligent Computing Theories and Applications With Aspects of Contemporary Intelligent Computing Techniques: 4th International Conference on ... in Computer and Information Science)

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J. Prod. Res. 32(10), 2431–2454 (1994) 8. : A Genetic Algorithm for Multiple Objective Sequencing Problems in Mixed Model Assembly Lines. Comput. Oper. Res. 25(7/8), 675–690 (1998) 9. : A Genetic-Algorithm-Based Optimization Model for Scheduling Flexible Assembly Lines. Int. J. Adv. Manuf. Technol. 1007/s00170-006-0818-6 10. : Multi-Criteria Sequencing Problem for a Mixed-Model Assembly Line in a JIT Production System. Appl. Math. Comput. 181, 1471–1481 (2006) 11. : Scheduling of an Assembly Line with a Multi-Objective Genetic Algorithm.

4) Stopping criterion. The stopping criterion of the AC algorithm could be the maximal number of running cycles or the CPU time limit. 3 Vehicle Routing Problem The Vehicle Routing Problem (VRP) is described as a weighted graph G= where the nodes are represented by N=(N0,N1,…,NM) and the arcs are represented by E={(Ni,Nj):i≠j}, shown as in Fig. 1. In this graph model, N0 is the central depot and the other nodes are the M customers to be served. Each node is associated with a fixed quantity qi of goods to be delivered (a quantity q0=0 is associated to the depot N0).

Cn Abstract. Vehicle routing problem becomes more remarkable with the development of modern logistics. Ant colony and genetic algorithm are combined for solving vehicle routing problem. GA can overcome the drawback of premature and weak exploitation capabilities of ant colony and converge to the global optimal quickly. The performance of the proposed method as compared to those of the genetic-based approaches is very promising. Keywords: ant colony, vehicle routing problem, genetic algorithm. 1 Introduction Many heuristic methods currently used in combinatorial optimization are inspired by adaptive natural behaviors or natural systems, such as genetic algorithms, simulated annealing, neural networks, etc.

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