By Ying Tan, Yuhui Shi, Li Li

This two-volume set LNCS 9712 and LNCS 9713 constitutes the refereed lawsuits of the seventh foreign convention on Swarm Intelligence, ICSI 2016, held in Bali, Indonesia, in June 2016. The a hundred thirty revised normal papers provided have been rigorously reviewed and chosen from 231 submissions. The papers are geared up in 22 cohesive sections protecting significant themes of swarm intelligence and comparable components resembling development and types of swarm intelligence study; novel swarm-based optimization algorithms; swarming behaviour; a few swarm intelligence algorithms and their functions; hybrid seek optimization; particle swarm optimization; PSO functions; ant colony optimization; mind typhoon optimization; fireworks algorithms; multi-objective optimization; large-scale worldwide optimization; biometrics; scheduling and making plans; desktop studying tools; clustering set of rules; type; picture type and encryption; facts mining; sensor networks and social networks; neural networks; swarm intelligence in administration selection making and operations examine; robotic regulate; swarm robotics; clever power and communications structures; and clever and interactive and tutoring structures.

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

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J. Parallel Prog. 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.

4 J. P. Engelbrecht times [2,3], break down intervals [4,5], fuzzy or stochastic input data [6,7], and the requirement for real-time rescheduling [4,8]. However, to the best of the authors’ knowledge, the multi-objective flexible job shop scheduling problem with sequence-dependent set-up times, auxiliary resources and machine down time has only been addressed in [9]. Significant opportunities for additional research into this complex problem thus exists. The resulting hyper-heuristic algorithm which was developed is based on the heterogeneous meta-hyper-heuristic (HMHH) algorithm of Grobler et al.

Technol. 27(5), 580–587 (2006) 18. : Genetic optimization of order scheduling with multiple uncertainties. Expert Syst. Appl. 35(4), 1788–1801 (2007) 19. : EvAg: a scalable peer-to-peer evolutionary algorithm. Genet. Program Evolvable Mach. 11, 227–246 (2010) 20. : A chaotic non-dominated sorting genetic algorithm for the multi-objective automatic test task scheduling problem. Appl. Soft Comput. 13(5), 2790– 2802 (2013) 21. : A variable neighborhood MOEA/D for multiobjective test task scheduling problem.

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