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 foreign convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers offered have been conscientiously reviewed and chosen from 298 submissions. The papers are prepared 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 approach, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy equipment, and hybrid algorithms - for half I. themes addressed partly II are comparable to multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent structures, facts mining tools, laptop studying tools, characteristic choice algorithms, development popularity equipment, clever keep an eye on, different optimization algorithms and purposes, information fusion and swarm intelligence, in addition to fish university seek - foundations and applications.
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Additional info for Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part II
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. In summary, the proposed multi-objective vehicle assignment model can reduce the dimension of the solution space and be easily adapted by MOPSO algorithms. Furthermore, the constrain treatment strategy, which considers the violations as an objective, is an effective method.
The multi-objective optimization (MOO) is a challenging problem due to the inherent confliction nature of objective to be optimized. As evolutionary algorithm can deal simultaneously with a set of possible solutions in a single run, it is especially suitable to solve MOO problems. Since Schaffer proposed a Vector Evaluated Genetic algorithm (VEGA) in 1984, many evolutionary MOO algorithms have been developed in the past decades. The most of studies published on multi-objective genetic algorithm (MOGA) and multiobjective evolutionary algorithm (MOEA) in different fashion, such as, SPEA2 , NSGA .
The operation of O X is similar to X KN . , xK⋅N+K⋅M ] =[XKN , XKM ] (7) Table 3. 3 The Maintenance of pBest and gBest The maintenance strategy of Pareto pool for both pBest and gbest is as follows: a. If the inferior solution particles dominate some of the solutions in the Pareto pool, delete the dominated particles, and join the current solution to the Pareto pool. b. The particle which be dominated by the Pareto pool particles is directly ignored. c. If the current particle and the Pareto pool particles have no relation of dominate, and the population of the Pareto pool did not reach the scale of the pool, then the particle will join the Pareto pool directly.