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 foreign 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 prepared in topical sections on theoretical research of swarm intelligence algorithms, particle swarm optimization, purposes of pso algorithms, ant colony optimization algorithms, bee colony algorithms, novel swarm-based optimization algorithms, synthetic immune method, differential evolution, neural networks, genetic algorithms, evolutionary computation, fuzzy equipment, and hybrid algorithms - for half I. subject matters addressed partly II are similar to multi-objective optimization algorithms, multi-robot, swarm-robot, and multi-agent structures, information mining equipment, laptop studying equipment, function choice algorithms, trend popularity equipment, clever keep an eye on, different optimization algorithms and functions, info fusion and swarm intelligence, in addition to fish institution 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
In order not to affect the tail of the gene, symbols are removed from the end of the head to make room for the inserted string. , split the chromosomes into three portions and swap the middle one. 5 Experiments and Results In this section, Simulation experiments are conducted on training and validating sets which comprise of several problem instances to create SRs and evaluate their performance. The problem instances are randomly generated according with the following method. The number of jobs is set to be 100.
The results indicate that our HMOPSO algorithm is competitive or superior to the NSGA-II, and much better than two MOPSO algorithms in the literature for all benchmark problems. Acknowledgements This research is supported by Key Program of National Natural Science Foundation of China (71032004), National Natural Science Foundation of China (70902065), National Science Foundation for Post-doctoral Scientists of China (20100481197), and the Fundamental Research Funds for the Central Universities (N090404018).
Liu (a) (b) (c) (d) Fig. 2. Pareto solutions of MOPSO and MO-CPSO. (a) SCH, (b) FON, (c) POL, (d)KUR. Correlative Particle Swarm Optimization for Multi-objective Problems 25 6 Conclusion In this paper, we propose MO-CPSO for multi-objective problems, where the correlative processing strategy is used to maintain population diversity, and the disturbance operation is adopted to improve convergence accuracy of solutions. Experimental results show that the proposed algorithm can find solutions with good diversity and convergence, and is an efficient approach for complex multi-objective optimization problems.