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The Scientific World Journal
Volume 2013 (2013), Article ID 860289, 12 pages
http://dx.doi.org/10.1155/2013/860289
Research Article

On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

School of Mathematics, Statistics, and Computer Science, University of Kwazulu-Natal, Private Bag X54001, Durban 4000, South Africa

Received 9 July 2013; Accepted 4 September 2013

Academic Editors: P. Melin, G. Terracina, and G. Wei

Copyright © 2013 Martins Akugbe Arasomwan and Aderemi Oluyinka Adewumi. 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 [14 citations]

The following is the list of published articles that have cited the current article.

  • Aderemi Oluyinka Adewumi, and Akugbe Martins Arasomwan, “An improved particle swarm optimiser based on swarm success rate for global optimisation problems,” Journal of Experimental & Theoretical Artificial Intelligence, pp. 1–43, 2014. View at Publisher · View at Google Scholar
  • Akugbe Martins Arasomwan, and Aderemi Oluyinka Adewumi, “An Investigation into the Performance of Particle Swarm Optimization with Various Chaotic Maps,” Mathematical Problems in Engineering, 2014. View at Publisher · View at Google Scholar
  • Martins Akugbe Arasomwan, and Aderemi Oluyinka Adewumi, “Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems,” Scientific World Journal, 2014. View at Publisher · View at Google Scholar
  • Aderemi Oluyinka Adewumi, and Akugbe Martins Arasomwan, “Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization,” International Journal on Artificial Intelligence Tools, vol. 24, no. 05, pp. 1550017, 2015. View at Publisher · View at Google Scholar
  • Sameh Kessentini, and Dominique Barchiesi, “Particle Swarm Optimization with Adaptive Inertia Weight,” International Journal of Machine Learning and Computing, vol. 5, no. 5, pp. 368–373, 2015. View at Publisher · View at Google Scholar
  • Micheal O. Olusanya, Martins A. Arasomwan, and Aderemi O. Adewumi, “Particle Swarm Optimization Algorithm for Optimizing Assignment of Blood in Blood Banking System,” Computational and Mathematical Methods in Medicine, vol. 2015, pp. 1–12, 2015. View at Publisher · View at Google Scholar
  • Mohammad Javad Amoshahy, Mousa Shamsi, and Mohammad Hossein Sedaaghi, “A novel flexible inertia weight particle swarm optimization algorithm,” PLoS ONE, vol. 11, no. 8, 2016. View at Publisher · View at Google Scholar
  • Jianhua Liu, Xiaodong Li, and Yi Mei, “An analysis of the inertia weight parameter for binary particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 20, no. 5, pp. 666–681, 2016. View at Publisher · View at Google Scholar
  • Mojtaba Taherkhani, and Reza Safabakhsh, “A novel stability-based adaptive inertia weight for particle swarm optimization,” Applied Soft Computing, vol. 38, pp. 281–295, 2016. View at Publisher · View at Google Scholar
  • Hongguang Li, Wenrui Ding, Xianbin Cao, and Chunlei Liu, “Image Registration and Fusion of Visible and Infrared Integrated Camera for Medium-Altitude Unmanned Aerial Vehicle Remote Sensing,” Remote Sensing, vol. 9, no. 5, pp. 441, 2017. View at Publisher · View at Google Scholar
  • Guo-Feng Fan, Meng-Qi Liang, Jing-Ru Li, and Wen-Lu Ma, “Applications of SVR-PSO Model and Multivariate Linear Regression Model in PM2.5 Concentration Forecasting,” International Journal of Applied Evolutionary Computation, vol. 8, no. 4, pp. 53–69, 2017. View at Publisher · View at Google Scholar
  • Jia Tang, Xuyang Wang, Hongjie Jia, Chengshan Wang, Zhanyong Yang, Dan Wang, Renle Huang, and Menghua Fan, “Study on day-ahead optimal economic operation of active distribution networks based on Kriging model assisted particle swarm optimization with constraint handling techniques,” Applied Energy, vol. 204, pp. 143–162, 2017. View at Publisher · View at Google Scholar
  • Xibin Wang, Fengji Luo, Chunyan Sang, Jun Zeng, and Sachio Hirokawa, “Personalized Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization,” IEICE Transactions on Information and Systems, vol. E100.D, no. 2, pp. 285–293, 2017. View at Publisher · View at Google Scholar
  • Zhao Yang, Ming-Qing Xiao, Lei Zhang, Xi-Lang Tang, Ya-Wei Ge, De-Long Feng, and Hai-Fang Song, “A double-loop hybrid algorithm for the traveling salesman problem with arbitrary neighbourhoods,” European Journal of Operational Research, vol. 265, no. 1, pp. 65–80, 2018. View at Publisher · View at Google Scholar