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 court cases of the overseas convention on Swarm Intelligence, ICSI 2011, held in Chongqing, China, in June 2011. The 143 revised complete papers awarded have been rigorously reviewed and chosen from 298 submissions. The papers are equipped 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, synthetic immune approach, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy equipment, and hybrid algorithms - for half I. subject matters addressed partially II are corresponding to multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent platforms, facts mining equipment, computer studying equipment, function choice algorithms, trend popularity equipment, clever regulate, different optimization algorithms and purposes, info fusion and swarm intelligence, in addition to fish tuition seek - foundations and applications.
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Additional resources for Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part II
In: 5th International Conference on Genetic Algorithms, pp. 416–423. Morgan Kaufmann, California (1993) 11. : A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In: 1st IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Computation, pp. 82–87. IEEE Press, New Jersey (1994) 12. : Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evol. Comput. 2(3), 221–248 (1994) 13. : Comparison of Multiobjective Evolutionary Algorithms: Empirical Results.
Shen, G. Wang, and Q. Liu multi-objective PSO (MOPSO) is the local guide (gbest) distribution must be redefined in order to obtain a set of non-dominated solutions (Pareto front). g. an adaptive-grid mechanism, an adaptive mutation operation. However, this results in desired diversity and convergence still not close enough to the Pareto front. In order to improve convergence and diversity of solutions, a correlative particle swarm optimization (CPSO) with disturbance operation is proposed, named MO-CPSO.
1 Introduction Many kinds of MO evolutionary algorithms (MOEAs) have been proposed and widely used. These MOEAs range from traditional EAs such as genetic algorithm (GA), evolution strategy, and genetic programming, to newly developed techniques such as the NSGA-II , the PAES , the SPEA2 , the MO scatter search , and so on. In recent years, many particle swarm optimization (PSO) algorithms have been proposed for solving MO problems, and the computational results show that PSO is very suitable for MO problems.
Advances in Swarm Intelligence: Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part II by Li Nie, Liang Gao, Peigen Li, Xiaojuan Wang (auth.), Ying Tan, Yuhui Shi, Yi Chai, Guoyin Wang (eds.)