Chapter

Foundations of Computational Intelligence Volume 3

Volume 203 of the series Studies in Computational Intelligence pp 101-128

Particle Swarm Optimization: Performance Tuning and Empirical Analysis

  • Millie PantAffiliated withDepartment of Paper Technology, IIT Roorkee
  • , Radha ThangarajAffiliated withDepartment of Paper Technology, IIT Roorkee
  • , Ajith AbrahamAffiliated withQ2S, Norwegian University of Science and Technology

* Final gross prices may vary according to local VAT.

Get Access

Abstract

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.