By Ying Tan, Yuhui Shi, Li Li
This two-volume set LNCS 9712 and LNCS 9713 constitutes the refereed court cases of the seventh overseas convention on Swarm Intelligence, ICSI 2016, held in Bali, Indonesia, in June 2016. The a hundred thirty revised average papers provided have been rigorously reviewed and chosen from 231 submissions. The papers are prepared in 22 cohesive sections masking significant issues of swarm intelligence and comparable parts corresponding to development and versions of swarm intelligence examine; 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; computing device studying equipment; clustering set of rules; type; photo category and encryption; information mining; sensor networks and social networks; neural networks; swarm intelligence in administration selection making and operations learn; robotic regulate; swarm robotics; clever strength and communications platforms; and clever and interactive and tutoring structures.
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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
J. Comput. Integr. Manuf. 28(5), 470–487 (2015) 4. : A bipartite genetic algorithm for multi-processor task scheduling. Int. 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.
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. Math. Probl. Eng. 2014 (2014). 423621 22. : A parallel genetic algorithm for a flexible job-shop scheduling problem with sequence dependent setups. Int. J. Adv. Manuf. Technol. 49(1), 263–279 (2010) 23. : Optimizing the multi-UUT parallel test task scheduling based on multi-objective GASA.
Chin. Space Sci. Technol. com Abstract. The test task scheduling problem (TTSP) is an essential issue in automatic test system. In this paper, a new non-integrated algorithm called GASCR which combines a genetic algorithm with a new rule for scheme selection is adopted to ﬁnd optimal solutions. GASCR is a hierarchal approach based on the characteristics of TTSP because the given problem can be decomposed into task sequence and scheme choice. GA with the non-Abelian (Nabel) crossover and stochastic tournament (ST) selector is used to ﬁnd a proper task sequence.