By Ying Tan, Yuhui Shi, Carlos Coello Coello

This e-book and its better half quantity, LNCS vol. 8794 and 8795 represent the court cases of the fifth overseas convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers offered have been conscientiously reviewed and chosen from 198 submissions. The papers are prepared in 18 cohesive sections, three specific classes and one aggressive consultation masking all significant themes of swarm intelligence study and improvement corresponding to novel swarm-based seek equipment; novel optimization set of rules; particle swarm optimization; ant colony optimization for traveling salesman challenge; synthetic bee colony algorithms; man made immune procedure; evolutionary algorithms; neural networks and fuzzy tools; hybrid tools; multi-objective optimization; multi-agent platforms; evolutionary clustering algorithms; class equipment; GPU-based equipment; scheduling and direction making plans; instant sensor networks; energy method optimization; swarm intelligence in snapshot and video processing; purposes of swarm intelligence to administration difficulties; swarm intelligence for real-world application.

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Extra info for Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part I

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The swarm behavior of pedestrians in a crowd, generally, causes a global pattern to emerge. A pedestrian crowd simulation system must have this emergence in order to prove its effectiveness. For this reason, the aim of our work is to demonstrate the effectiveness of our model PHuNAC (Personalities’ Human's Nature of Autonomous Crowds) and also prove that the swarm behavior of pedestrians’ agents in our model allows the emergence of these global patterns. In order to validate our approach, we compared our system with real data.

Then, the internal learning rate of X Ki can be given as rule (3):  B in a r y: B r a te Ki = B r a te ′ − H D k , h L e n g th + 1  i  F lo a t: B r a te K = B r a te ′ − E D k , h R a d iu s + 1 (3) Wherein, Brate′ stands for the initial value of the internal learning rate; H D K , h stands for the Hamming distance between X Ki and X Kbest ; Length stands for the length of the gene expression encoding; ED K , h stands for the Euclidean distance between X Ki and X Kbest , namely, E D k , h =  n 1 search space, that is, R a d iu s = ( x ib e s t − x i ) 2 .

When flows cross vertically, diagonal stripe pattern emerges and the congestion degree of each flows varies in the crossing area [22]. PHuNAC Model: Emergence of Crowd’s Swarm Behavior 15 In the second experiment, we simulate the crossing flows. Fig. 3 shows a screenshot of the simulation. We observed that a diagonal stripe pattern emerged after two flows have crossed. This result is consistent with the phenomenon mentioned in [22]. Fig. 3. Diagonal stripe pattern emerges. 2 Quantitative Experiments We present in this section quantitative experiments.

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