A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms
作者： Han Duy Phan , Kirsten Ellis , Jan Carlo Barca , Alan Dorin
DOI: 10.1007/S00521-019-04229-2
关键词: Algorithm 、 Swarm intelligence 、 Evolutionary algorithm 、 Computer science 、 Nature inspired 、 Computation
摘要: … algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms … setting techniques for evolutionary algorithms. Counterparts providing …
springer.com
sci-hub.se 
researchgate.net 参考文章(139)
Morten Løvbjerg, Thiemo Krink, Thomas Kiel Rasmussen, Hybrid Particle Swarm Optimiser with breeding and subpopulations genetic and evolutionary computation conference. pp. 469- 476 ,(2001)
Tim Blackwell, Jürgen Branke, Multi-swarm Optimization in Dynamic Environments Lecture Notes in Computer Science. pp. 489- 500 ,(2004) , 10.1007/978-3-540-24653-4_50
Konstantinos E. Parsopoulos, Thomas Beielstein, Michael N. Vrahatis, Tuning PSO Parameters Through Sensitivity Analysis Universität Dortmund. ,(2002) , 10.17877/DE290R-15305
Xiang-han Chen, Wie-Ping Lee, Chen-Yi Liao, Jang-Ting Dai, None, Adaptive constriction factor for location-related particle swarm EC'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Evolutionary Computing - Volume 8. pp. 307- 313 ,(2007)
K. Deb, Optimization for Engineering Design: Algorithms and Examples ,(2004)
Priti Srinivas Sajja, Rajendra Akerkar, Bio-Inspired Models for Semantic Web Swarm Intelligence and Bio-Inspired Computation#R##N#Theory and Applications. pp. 273- 294 ,(2013) , 10.1016/B978-0-12-405163-8.00012-0
A. E. Eiben, S. K. Smit, Evolutionary Algorithm Parameters and Methods to Tune Them Autonomous Search. pp. 15- 36 ,(2011) , 10.1007/978-3-642-21434-9_2
Choosak Pornsing, Manbir S. Sodhi, Bernard F. Lamond, Novel self-adaptive particle swarm optimization methods soft computing. ,vol. 20, pp. 3579- 3593 ,(2016) , 10.1007/S00500-015-1716-3
Seyedali Mirjalili, Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems Neural Computing and Applications. ,vol. 27, pp. 1053- 1073 ,(2016) , 10.1007/S00521-015-1920-1
Seyedali Mirjalili, Moth-flame optimization algorithm Knowledge Based Systems. ,vol. 89, pp. 228- 249 ,(2015) , 10.1016/J.KNOSYS.2015.07.006