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Applied Computational Intelligence and Soft Computing
Volume 2011 (2011), Article ID 980216, 19 pages
Research Article

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 [3 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. View at Publisher · View at Google Scholar
  • 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. View at Publisher · View at Google Scholar
  • 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. View at Publisher · View at Google Scholar