By Li Nie, Liang Gao, Peigen Li, Xiaojuan Wang (auth.), Ying Tan, Yuhui Shi, Yi Chai, Guoyin Wang (eds.)
The two-volume set (LNCS 6728 and 6729) constitutes the refereed lawsuits of the overseas convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers provided have been conscientiously reviewed and chosen from 298 submissions. The papers are geared up in topical sections on theoretical research of swarm intelligence algorithms, particle swarm optimization, functions of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, man made immune process, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy tools, and hybrid algorithms - for half I. themes addressed partially II are reminiscent of multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent platforms, facts mining equipment, computer studying tools, characteristic choice algorithms, trend reputation equipment, clever regulate, different optimization algorithms and purposes, facts fusion and swarm intelligence, in addition to fish college seek - foundations and applications.
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Extra resources for Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part II
14 A. Di-Ming et al. 4 The Details of MOPSO Algorithm The Details of MOPSO algorithm as follows: a. Initialize P[i] randomly(P is the population of particles), the speed of each particle V[i], the maximum number of iterations T and the parameters as Table 2, Create external archive A that stores nondominated solutions null; b. If T=0, Store the nondominated vectors found in P into A. c. The values of objective functions based on the position of P[i] are calculated in P and A. d. Updating of pBest.
The future work is going to refine the model for more complicate scenarios and improve algorithm’s flexibility, stability and distribution uniformity for more tasks. 60903005). Special thanks go to Dr. Russ Eberhart, Xiaohui HU and Yaobin Chen at Indiana University-Purdue University Indianapolis for their assistance and collaboration. References 1. : Complexity in UAV cooperative control. In: American Control Conference, ACC, pp. 1831–1836 (2002) 2. : Optimization of Air Vehicle Operations Using Mixed-Integer Linear Programming.
0 Table 8. The UAV received value on performing the different tasks Vehicles Vehicle1 Vehicle2 Vehicle3 Vehicle4 Received value P Task-1 Task-2 18 10 10 20 5 12 10 4 Table 9. 42261 710 0 16 A. Di-Ming et al. 5 Conclusion Many of the experiments with different scenarios have been tested, each scenario, a set of Pareto optimal can be achieved. On the other hand, the Pareto front are not continue, since the existence of constrain requirements which is the third fitness J3 which determines the feasible solutions are limited.