Advertisement
Published in:
2009 | OriginalPaper | Chapter
Particle Swarm Optimization: Performance Tuning and Empirical Analysis
Authors : Millie Pant, Radha Thangaraj, Ajith Abraham
Published in: Foundations of Computational Intelligence Volume 3
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
This chapter presents some of the recent modified variants of Particle Swarm Optimization (PSO). The main focus is on the design and implementation of the modified PSO based on diversity, Mutation, Crossover and efficient Initialization using different distributions and Low-discrepancy sequences. These algorithms are applied to various benchmark problems including unimodal, multimodal, noisy functions and real life applications in engineering fields. The effectiveness of the algorithms is discussed.