Advances in Swarm Intelligence: 5th International by Ying Tan, Yuhui Shi, Carlos Coello Coello

By Ying Tan, Yuhui Shi, Carlos Coello Coello

This ebook and its spouse quantity, LNCS vol. 8794 and 8795 represent the lawsuits of the fifth overseas convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers awarded have been rigorously reviewed and chosen from 198 submissions. The papers are geared up in 18 cohesive sections, three detailed classes and one aggressive consultation masking all significant issues of swarm intelligence learn and improvement equivalent to novel swarm-based seek tools; novel optimization set of rules; particle swarm optimization; ant colony optimization for vacationing salesman challenge; synthetic bee colony algorithms; synthetic immune approach; evolutionary algorithms; neural networks and fuzzy tools; hybrid equipment; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; class tools; GPU-based equipment; scheduling and course making plans; instant sensor networks; strength method optimization; swarm intelligence in snapshot and video processing; functions of swarm intelligence to administration difficulties; swarm intelligence for real-world program.

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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 (Lecture Notes in Computer Science)

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For example, an individual may carry out crossover operation with one more individuals in the search process, yet its corresponding objective function value did not return to the program immediately. And due to the impact of random selection, after cross-operation was completed, some individuals within the population may be not involved in the crossover operation and their gene structures did not change, while some individuals within the population may conduct cross-operation for many times and their good gene structure may have been damaged.

The rest of this paper is organized as follows: In Section 2, we give description of the 3FNLS algorithm. Then our improvement strategy is described in Section 3. Section 4 is the computational results and comparisons. Conclusion and future work are finally presented in Section 5. 2 3FNLS Review To make this article self-contained, it is necessary to introduce the basic concepts of 3NFLS before presenting our improvement method. However, because the Improve the 3-flip Neighborhood Local Search 29 ideas in 3FNLS are complicated, we suggest the readers to refer [18] for the details.

He has also pointed out laws describing the relation between crowd flow and crowd density. 01 pedestrians/ m². Subsequently, we measured the average velocity of the crowd. 02 pedestrians /m². Similarly, we measured the average velocity of the crowd. We repeated this experiment several times. At the end of the experiment, we obtained a curve of average velocity as a function of density. 4 compares the curves of the average velocity of the PHuNAC model with curve representing the real data (Fruin [31]).

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