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 ordinary papers awarded have been rigorously reviewed and chosen from 231 submissions. The papers are geared up in 22 cohesive sections protecting significant issues of swarm intelligence and similar parts corresponding to development and types 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 typhoon optimization; fireworks algorithms; multi-objective optimization; large-scale worldwide optimization; biometrics; scheduling and making plans; desktop studying equipment; clustering set of rules; class; picture class and encryption; information mining; sensor networks and social networks; neural networks; swarm intelligence in administration determination making and operations study; robotic keep watch over; swarm robotics; clever strength and communications platforms; 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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5 CPSO DPSO 0 5 10 15 20 25 30 The iteration number 35 40 45 50 Fig. 4. Algorithm performance comparison The convergence rate of the two algorithms and the performance of solution in Fig. 4, we know that the convergence behavior of CDPSO is better than DPSO, and it can quickly find the optimal solution. From the solution set of CDPSO changed with iterations number, it has converged to the optimal solution direction of less evolutionary generation, and this indicates that it has a higher optimize efficiency.

For this scenario, we used DPSO and CDPSO to simulate the above question respectively, and compared their simulation results. Figure 3 shows the fitness change of the best assignment, the average assignment and the worst assignment in 50 iterations based on improved CDPSO. The x axis is the iteration number and the y axis is the fitness of particles. Table 1 represents the ultimate performance comparison results of the servicing spacecraft task allocation between DPSO and CDPSO. 5 -1 5 10 15 20 25 30 The iteration number 35 Fig.

Assume Pij ¼ Pkj to simplify the problem. The optimization objective is the maximal test completion time and it is as follows. f ¼ max Cjk 1 k 1 j kj N Here, the notification Cjk ¼ max Cji is the test completion time of tj for wkj . ri 2wkj ð3Þ 22 J. Shi et al. 3 Proposed Method The hierarchical method GASCR is based on the decomposition of TTSP into task sequence and scheme choice. GA focuses on the sequence of tasks using its advantage in the global search to investigate a diverse range of optimum task sequences.

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