By Ying Tan, Yuhui Shi, Carlos A Coello Coello
This booklet and its significant other quantity, LNCS vol. 8794 and 8795 represent the court cases of the fifth overseas convention on Swarm Intelligence, ICSI 2014, held in Hefei, China in October 2014. The 107 revised complete papers offered have been conscientiously reviewed and chosen from 198 submissions. The papers are equipped in 18 cohesive sections, three designated classes and one aggressive consultation protecting all significant issues of swarm intelligence learn and improvement akin to novel swarm-based seek equipment; novel optimization set of rules; particle swarm optimization; ant colony optimization for traveling salesman challenge; synthetic bee colony algorithms; synthetic immune process; evolutionary algorithms; neural networks and fuzzy equipment; hybrid tools; multi-objective optimization; multi-agent structures; evolutionary clustering algorithms; category equipment; GPU-based equipment; scheduling and course making plans; instant sensor networks; energy procedure optimization; swarm intelligence in photograph and video processing; purposes of swarm intelligence to administration difficulties; swarm intelligence for real-world application.
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Extra resources for Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II
Temporally adaptive estimation of logistic classifiers on data streams. Advances in Data Analysis and Classification 3(3), 243–261 (2009) 6. : On the window size for classification in changing environments. Intelligent Data Analysis 13(6), 861–872 (2009) 7. : Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams. In: IEEE 10th International Conference on Data Mining (ICDM), pp. 1175–1180 (2010) 8. : Enabling fast prediction for ensemble models on data streams. In: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, pp.
0 , population size P = 20, maximum iteration T = 500, velocity Vmax = 4, Vmin = −4 . 0 , population size P = 20, maximum iteration T = 500, velocity Vmax = 4, Vmin = −4 . These parameters are chosen based on the literature . 618. 1 according to the reference . The fitness function of GARS, PSORS, CBPSORS and AFSARS are defined as the equation (9). In CBPSORS, the core of feature set needs to compute, after that the population is initialized, and the operation is the same as AFSARS. The results achieved from 3 independent runs are employed in terms of the number of the evolved feature subsets in this paper.
In the preying behavior, when the AF current state is X i , it needs to select a state Yj randomly in its visual scope. If Yi < Yj , it moves forward a step in 30 F. Wang, J. Xu, and L. Li this direction. Otherwise, it selects randomly a state X j again in its visual distance, and it judges whether the forward condition is satisfied. If it can satisfy before trynumber times, it moves a step toward the state X j , otherwise, it moves a step randomly. When the AF selects to go forward a step in this direction, the mutation operation of genetic algorithm is adopted in the proposed AFSARS.
Advances in Swarm Intelligence: 5th International Conference, ICSI 2014, Hefei, China, October 17-20, 2014, Proceedings, Part II by Ying Tan, Yuhui Shi, Carlos A Coello Coello