By Ying Tan, Yuhui Shi, Carlos Coello Coello
This publication and its significant other 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 equipped in 18 cohesive sections, three distinct classes and one aggressive consultation overlaying all significant subject matters of swarm intelligence examine and improvement comparable to novel swarm-based seek equipment; novel optimization set of rules; particle swarm optimization; ant colony optimization for vacationing salesman challenge; man made bee colony algorithms; synthetic immune procedure; evolutionary algorithms; neural networks and fuzzy equipment; hybrid equipment; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; category tools; GPU-based tools; scheduling and direction making plans; instant sensor networks; energy approach optimization; swarm intelligence in photograph and video processing; purposes of swarm intelligence to administration difficulties; swarm intelligence for real-world application.
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Extra resources for Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part I
The design of the pedestrian behavior is based on psychosocial and psychophysical studies of normative personalities. In a second time, in order to have more realistic simulation, we opted for the PHuNAC  (Personalities’ Human's Nature of Autonomous Crowds) version which integrates heterogeneous crowd. 3 PHuNAC Model In our PHuNAC model ([4, 5]), each pedestrian is represented by an agent. Each agent has its own autonomous behavior and personality. In the following sections, we present firstly the personality of pedestrians in our model and secondly the behavior of each pedestrian.
However, a great number of these models do not cover all the psychological factors necessary for a pedestrian located in a crowd (see  and ). Moreover, many of these models simulate only homogenous personalities of pedestrians. In the reality, there are different personalities. Experimentations’ results of these models show that they lack realism. For this purpose, the goal of our work is to reproduce realistic collective behavior simulated situations by simulating a realistic pedestrian behavior and personality.
Com Abstract. According to benchmark learning theory in business management, a kind of competitive learning mechanism based on dynamic niche was set up. First, by right of imitation and learning, all the individuals within population were able to approach to the high yielding regions in the solution space, and seek out the optimal solutions quickly. Secondly, the premature convergence problem got completely overcame through new optimal solution policy. Finally, the algorithm proposed here is naturally adaptable for the dynamic optimization problems.
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