EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 119 (2017) MATEC Web Conf., 119 (2017) 01046 References
Open Access
Issue
MATEC Web Conf.
Volume 119, 2017
The Fifth International Multi-Conference on Engineering and Technology Innovation 2016 (IMETI 2016)
Article Number 01046
Number of page(s) 11
DOI https://doi.org/10.1051/matecconf/201711901046
Published online 04 August 2017
  1. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs 3rd ed (New York, 1999) [Google Scholar]
  2. I. Zelinka, A survey on evolutionary algorithms dynamics and its complexity - Mutual relations, past, present and future, Swarm and Evolutionary Computation, 25 (1), 2-14 (2015) [Google Scholar]
  3. B. Kazimipour and X. Li, A.K. Qin, A review of population initialization techniques for evolutionary algorithms, Proceedings of IEEE Congress on Evolutionary Computation, 2585-2592 (2014) [Google Scholar]
  4. M. Crepinsek, S.H. Liu, and M. Mernik, Exploration and exploitation in evolutionary algorithms: A survey, ACM Computing Surveys, 45 (3), 35 (2013) [CrossRef] [Google Scholar]
  5. J. Prakash, P.K. Singh, Partitional algorithms for hard clustering using evolutionary and swarm intelligence methods: a survey, Advances in Intelligent Systems and Computing, 2, 515-528 (2013) [CrossRef] [Google Scholar]
  6. B. Li, J. Li, K. Tang, and X. Yao, Many-objective evolutionary algorithms: a survey, ACM Computing Surveys, 48 (1), A10 (2015) [Google Scholar]
  7. H. Ishibuchi, H. Masuda, Y. Tanigaki, and Y. Nojima, Review of coevolutionary developments of evolutionary multi-objective and many-objective algorithms and test problems, IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making (MCDM), 178-184 (2014) [Google Scholar]
  8. V.L. Vachhani, V.K. Dabhi, and H.B. Prajapati, Survey of multi objective evolutionary algorithms, IEEE International Conference on Circuit, Power and Computing Technologies (2015) [Google Scholar]
  9. C. Von Lücken, B. Barán, and C. Brizuela, A survey on multi-objective evolutionary algorithms for many-objective problems, Computational Optimization and Applications, 58 (3), 707-756 (2014) [Google Scholar]
  10. R.V. Devi, S.S. Sathya, and M.S. Coumar, Evolutionary algorithms for de novo drug design-a survey, Applied Soft Computing Journal, 27, 543-552 (2015) [CrossRef] [Google Scholar]
  11. P.M. Pradhan and G. Panda, Comparative performance analysis of evolutionary algorithm based parameter optimization in cognitive radio engine: a survey, Ad Hoc Networks, 17, 129-146 (2014) [CrossRef] [Google Scholar]
  12. M. Gen and L. Lin, Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey, Journal of Intelligent Manufacturing, 25 (5), 849-866 (2014) [CrossRef] [Google Scholar]
  13. S. Li, L. Kang, and X.M. Zhao, A survey on evolutionary algorithm based hybrid intelligence in bioinformatics, Bio Med Research International, 2014 (2014) [Google Scholar]
  14. D.G.N. Rani and S. Rajaram, A survey on B*-Tree-based evolutionary algorithms for VLSI floorplanning optimisation, International Journal of Computer Applications in Technology, 48 (4), 281-287 (2013) [CrossRef] [Google Scholar]
  15. A. Ponsich, A.L. Jaimes, and C.A.C. Coello, A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications, IEEE Transactions on Evolutionary Computation, 17 (3), 321-344 (2013) [CrossRef] [Google Scholar]
  16. Y.C. Lin, K.S. Hwang, and F.S. Wang, Plant scheduling and planning using mixed-integer hybrid differential evolution with multiplier updating, Proceedings of the IEEE Conference on Evolutionary Computation, 1, 593-600 (2000) [Google Scholar]
  17. J.P. Chiou and F.S. Wang, Hybrid method of evolutionary algorithms for static and dynamic optimization problems with application to fed-batch fermentation process, Computers & Chemical Engineering, 23, 1277-1291 (1999) [CrossRef] [Google Scholar]
  18. J.P. Chiou, C.F. Chang, and C.T. Su, Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems, IEEE Transactions on Power Systems, 20 (2), 668-674 (2005) [CrossRef] [Google Scholar]
  19. J.P. Chiou, Variable scaling hybrid differential evolution for large-scale economic dispatch problems, Electric Power Systems Research, 77, 212-218 (2007) [CrossRef] [Google Scholar]
  20. T. Back, F. Hoffmeister, and H.P. Schwefel, A survey of evolution strategies, Proc. of 4th Int. Conf. Genetic Algorithms, 2-9 (1991) [Google Scholar]
  21. T. Back and H.P. Schwefel, An overview of evolutionary algorithms for parameter optimization, Evol. Comput., 1, 1-23 (1993) [CrossRef] [Google Scholar]
  22. D. Sudha Rani, N. Subrahmanyam, and M. Sydulu, Multi-objective invasive weed optimization-An application to optimal network reconfiguration in radial distribution systems, International Journal of Electrical Power and Energy Systems, 73, 932-942 (2015) [CrossRef] [Google Scholar]
  23. H. Huang, J. Gu, and C. Fang, Application of undirected spanning tree-based parallel genetic algorithm in distributed network reconfiguration, Dianli Xitong Zidonghua/Automation of Electric Power Systems, 39 (14), 89-96 (2015) [Google Scholar]
  24. F.C. Liu, W. Xu, G. Zhang, W.Z. Wang, and Z.Y. Li, Distribution network reconfiguration based on immune clonal selection algorithm, Environment, Proc. of 3rd International Conference on Frontier of Energy and Environment Engineering, 657-660 (2015) [Google Scholar]
  25. T.T. Nguyen and A.V. Truong, Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm, International Journal of Electrical Power and Energy Systems, 68, 233-242 (2015) [Google Scholar]
  26. R. Syahputra, I. Robandi, and M. Ashari, PSO based multi-objective optimization for reconfiguration of radial distribution network, International Journal of Applied Engineering Research, 10 (6), 14573-14586 (2015) [Google Scholar]
  27. K. Sureshkumar nad P. Vijayakumar, Distribution network reconfiguration for loss minimisation using differential evolution algorithm, ARPN Journal of Engineering and Applied Sciences, 10 (7), 2861-2866 (2015) [Google Scholar]
  28. R. Iswarya and R.M. Sasiraja, Network reconfiguration of distribution system in presence of harmonic load using expert system approach, International Journal of Applied Engineering Research, 10 (55), 1961-1966 (2015) [Google Scholar]
  29. P. Civicioglu, Backtracking Search Optimization Algorithm for numerical optimization problems, Applied Mathematics and Computation, 219, 8121-8144 (2013) [CrossRef] [Google Scholar]
  30. R. Storn and K.V. Price, Minimizing the real functions of the ICEC ’96 contest by differential evolution, IEEE Conference on Evolutionary Computation, 842-844 (1996) [CrossRef] [Google Scholar]
  31. K.V. Price, Differential evolution vs. functions of the 2nd ICEC, IEEE Conference on Evolutionary Computation, 153-157 (1997) [Google Scholar]
  32. C.T. Su and C.C. Tsai, A new fuzzy reasoning approach to optimum capacitor allocation for primary distribution systems, Proc. IEEE on Industrial Technology Conf., 237-241 (1996) [Google Scholar]
  33. C.T. Su, C.S. Lee, and C.S. Ho, Optimal selection of capacitors in distribution systems, Proc. IEEE Power Tech. Conf., 301 (1999) [Google Scholar]
  34. B. Goffe, Global optimization of statistical functions with simulated annealing, Journal of Economics, 60 (12), 65-100 (1994) [Google Scholar]
  35. S. Civanlar, J.J. Grainger, H. Yin, and S.S.H. Lee, Distribution feeder reconfiguration for loss reduction, IEEE Trans. Power Delivery, 3, 1217-1223 (1988) [CrossRef] [Google Scholar]
  36. H.C. Cheng and C.C. Kou, Network reconfiguration in distribution systems using simulated annealing, Electric Power System Research, 29, 227-238 (1994) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.