By De-Shuang Huang, Kang-Hyun Jo, Abir Hussain
This two-volume set LNCS 9225 and LNCS 9226 constitutes - along side the amount LNAI 9227 - the refereed complaints of the eleventh overseas convention on clever Computing, ICIC 2015, held in Fuzhou, China, in August 2015. The 191 complete papers and forty two brief papers of this quantity have been conscientiously reviewed and chosen from 191 submissions. The papers are equipped in topical sections similar to evolutionary computation and studying; compressed sensing, sparse coding and social computing; neural networks, nature encouraged computing and optimization; development popularity and sign processing; photo processing; biomedical informatics idea and strategies; differential evolution, particle swarm optimization and area of interest expertise; clever computing and information discovery and knowledge mining; tender computing and desktop studying; computational biology, protein constitution and serve as prediction; genetic algorithms; man made bee colony algorithms; swarm intelligence and optimization; social computing; details protection; digital truth and human-computer interplay; healthcare informatics idea and techniques; unsupervised studying; collective intelligence; clever computing in robotics; clever computing in verbal exchange networks; clever keep an eye on and automation; clever facts research and prediction; gene expression array research; gene rules modeling and research; protein-protein interplay prediction; biology encouraged computing and optimization; research and visualization of enormous organic info units; motif detection; biomarker discovery; modeling; simulation; and optimization of organic platforms; biomedical information modeling and mining; clever computing in biomedical signal/image research; clever computing in mind imaging; neuroinformatics; cheminformatics; clever computing in computational biology; computational genomics; unique consultation on biomedical info integration and mining within the period of massive facts; unique consultation on huge info analytics; distinct consultation on man made intelligence for ambient assisted dwelling; and targeted consultation on swarm intelligence with discrete dynamics.
Read Online or Download Intelligent Computing Theories and Methodologies: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015, Proceedings, Part II PDF
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Extra info for Intelligent Computing Theories and Methodologies: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015, Proceedings, Part II
Therefore, in this paper, we studied OTRAPs with the goal to minimize the testing cost with the constraint of reliability and testing resource. There many optimization approaches have been proposed to solve OTRAPs, like dynamic programming , and nonlinear programming [12–14]. When the solution space of the problem is multimodal, these methods may be easily trapped in a local optimal. Genetic algorithms (GA) are inspired by Darwin’s theory of evolution. GA has a strong capability of global search which will escape from local optima.
Acknowledgement. The work in this paper is in part supported by Wuhan University of Science and Technology city college scientiﬁc research project under Grant No. 2014cyybky011. References 1. : Artiﬁcial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems. , Pedrycz, W. ) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007) 2. : A powerful and efﬁcient algorithm for numerical function optimization: artiﬁcial bee colony (ABC) algorithm. J.
2 shows the convergence curve of following typical test functions named f1, f2, f4, f7, f8 and f9. From the curves in Fig. 2 it can be seen that PSO-DE-GABC and PSO-DE-PABC have faster convergence speed and better convergence accuracy. Fig. 2. Comparison of convergence process for six functions 34 F. Zhou and Y. Yang 5 Conclusion Inspired by particle swarm optimization and differential evolution, this paper proposes PSO-DE-GABC and PSO-DE-PABC algorithms on the basis of global convergence ability and convergence speed, and introduces a simple and effective parameter (DF, Dimension Factor) to control the convergence speed of these two search strategies.
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