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

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 one hundred thirty revised typical papers provided have been conscientiously reviewed and chosen from 231 submissions. The papers are equipped in 22 cohesive sections masking significant subject matters of swarm intelligence and similar parts akin to 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 hurricane optimization; fireworks algorithms; multi-objective optimization; large-scale international optimization; biometrics; scheduling and making plans; computer studying tools; clustering set of rules; class; photograph category and encryption; facts mining; sensor networks and social networks; neural networks; swarm intelligence in administration selection making and operations examine; robotic keep watch over; swarm robotics; clever strength and communications structures; and clever and interactive and tutoring structures.

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Tan et al. ): ICSI 2016, Part II, LNCS 9713, pp. 19–27, 2016. 1007/978-3-319-41009-8_3 20 J. Shi et al. , which combine tabu search and firefly algorithms to solve capacitated job shop scheduling problem [3]. Bonyadi split the problem into two parts. The first part contains a population of processor numbers while the second one includes a population of task sequences [4]. In summary, the hierarchical structure divides the problem into several independent problems, all of which can be modified or improved without affecting another one.

The problem thus focuses on determining both an assignment and a sequence of the operations on all machines that minimize some criteria. In addition, production calendars and resource down time intervals were addressed as described in [9]. Three objectives need to be minimized, namely makespan (max fi ∀i ∈ L ), n earliness/tardiness ( i=1 max{0, νi − −fi } + max{0, fi − νi }∀i ∈ L ) and queue J time ( j=1 max{0, fj − τj − pj }). Here νi denotes the due date of operation i, L is the set containing the last operations of all jobs and Υ is the set containing the first operations of all jobs.

Keywords: On-orbit servicing Á Spacecraft mission planning Á Discrete particle swarm optimization Á Chaos 1 Introduction The on-orbit servicing (OOS) technology is the use of automated technology and robot technology for the on-orbit inspection, repair, refueling, upgrade, maintenance, assembling and release by the use of automation and robotics [1, 2]. The OOS technology can effectively prolong the life of spacecraft and reduce the costs. However, the carrying capacity of space vehicle is strictly limited.

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