Download Advances in Swarm Intelligence: 7th International by Ying Tan, Yuhui Shi, Li Li PDF

By Ying Tan, Yuhui Shi, Li Li

This two-volume set LNCS 9712 and LNCS 9713 constitutes the refereed court cases of the seventh foreign convention on Swarm Intelligence, ICSI 2016, held in Bali, Indonesia, in June 2016. The one hundred thirty revised general papers awarded have been conscientiously reviewed and chosen from 231 submissions. The papers are geared up in 22 cohesive sections overlaying significant themes of swarm intelligence and comparable components comparable to development and versions of swarm intelligence study; novel swarm-based optimization algorithms; swarming behaviour; a few swarm intelligence algorithms and their purposes; hybrid seek optimization; particle swarm optimization; PSO functions; ant colony optimization; mind hurricane optimization; fireworks algorithms; multi-objective optimization; large-scale international optimization; biometrics; scheduling and making plans; desktop studying equipment; clustering set of rules; type; picture type and encryption; information mining; sensor networks and social networks; neural networks; swarm intelligence in administration choice making and operations learn; robotic keep watch over; swarm robotics; clever strength and communications structures; and clever and interactive and tutoring platforms.

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Additional info for Advances in Swarm Intelligence: 7th International Conference, ICSI 2016, Bali, Indonesia, June 25-30, 2016, Proceedings, Part II

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By adopting tabu search algorithm [5], a client was removed from one route and inserted into another vehicle’s route with minimum cost in each iteration. Taniguchi et al. [6] adjusted and optimized the existing routes according to real-time road traffic information. An improved genetic algorithm using the novel crossover operator [7] was constructed so as to avoid the premature convergence. Liu [8] focused on the time dynamics of the vehicle. The time-depended vehicle routing problem was modeled and two methods to solve dynamic vehicle routing problems with dynamic demands were presented.

The scheme number of t3 is 1. The final result is SC¼f 2 1 3 1 g. Table 1. 3 Time Task Scheme 5 t3 w13 3 w14 6 t4 w24 2 w34 Source Time r4 8 r1 ; r3 4 r2 ; r4 11 r2 ; r3 4 Performance Evaluation A common evaluation metric for the performance of algorithms is the best value (BV). However, it is not comprehensive for the TTSP. The following metrics are used to assess the results. 24 J. Shi et al. 1. The success rate (SR) measures the proportion of runs in which the algorithm is able to find the problem-specific optimum.

37(5), 462–487 (2009) 5. : An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem. Int. J. Prod. Res. 51(3), 641–651 (2013) 6. : An ant colony optimization algorithm for load balancing in parallel machines with sequence-dependent setup times. Comput. Oper. Res. 39 (6), 1225–1235 (2012) 7. : An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem. Flex. Serv. Manuf. J. 23(1), 64–85 (2011) 8. : Development a new mutation operator to solve the traveling salesman problem by aid of genetic algorithms.

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