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Applied Computational Intelligence and Soft Computing
Volume 2011 (2011), Article ID 980216, 19 pages
A Probability Collectives Approach with a Feasibility-Based Rule for Constrained Optimization
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
Received 6 May 2011; Accepted 6 September 2011
Academic Editor: R. Saravanan
Copyright © 2011 Anand J. Kulkarni and K. Tai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citations to this Article [4 citations]
The following is the list of published articles that have cited the current article.
- Anand J. Kulkarni, and Hinna Shabir, “Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm,” International Journal of Machine Learning and Cybernetics, 2014.
- Neha S. Patankar, Anand J. Kulkarni, Kang Tai, T. D. Ghate, and A. R. Parvate, “Multi-criteria probability collectives,” International Journal Of Bio-Inspired Computation, vol. 6, no. 6, pp. 369–383, 2014.
- Bo Yang, and Ruiming Wu, “A modified probability collectives optimization algorithm based on trust region method and a new temperature annealing schedule,” Soft Computing, vol. 20, no. 4, pp. 1581–1600, 2015.
- Zixiang Xu, Ahmet Unveren, and Adnan Acan, “Probability collectives hybridised with differential evolution for global optimisation,” International Journal Of Bio-Inspired Computation, vol. 8, no. 3, pp. 133–153, 2016.