Influence of fitness quantization noise on the performance of interactive PSO

Yu Nakano, Hideyuki Takagi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

We analyze the influence of quantization noise in fitness values on the search performance of Particle Swarm Optimization (PSO) and propose methods for reducing the negative influence of the noise to help realize a practical Interactive PSO. First, we compare the convergences of PSO and genetic algorithms (GA) with several different levels of quantized fitness values and show that PSO has a higher sensitivity to quantization noise than GA. Second, we analyze the sensitivity of each of the three components that determine the subsequent generation's PSO velocities and show that the sensitivities of the three components are almost equivalent.This implies that we need to develop methods for reducing the effect of quantization noise on all three components of the PSO velocity. As one of the solution, we propose a method using the average location of multiple global bests of same fitness value and another method for multimodal searching spaces using subglobal bests obtained by clustering.

Original languageEnglish
Title of host publication2009 IEEE Congress on Evolutionary Computation, CEC 2009
Pages2416-2422
Number of pages7
DOIs
Publication statusPublished - Nov 25 2009
Event2009 IEEE Congress on Evolutionary Computation, CEC 2009 - Trondheim, Norway
Duration: May 18 2009May 21 2009

Publication series

Name2009 IEEE Congress on Evolutionary Computation, CEC 2009

Other

Other2009 IEEE Congress on Evolutionary Computation, CEC 2009
CountryNorway
CityTrondheim
Period5/18/095/21/09

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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    Nakano, Y., & Takagi, H. (2009). Influence of fitness quantization noise on the performance of interactive PSO. In 2009 IEEE Congress on Evolutionary Computation, CEC 2009 (pp. 2416-2422). [4983243] (2009 IEEE Congress on Evolutionary Computation, CEC 2009). https://doi.org/10.1109/CEC.2009.4983243