A novel search interval forecasting optimization algorithm

Yang LOU*, Junli LI, Yuhui SHI, Linpeng JIN

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)

Abstract

In this paper, we propose a novel search interval forecasting (SIF) optimization algorithm for global numerical optimization. In the SIF algorithm, the information accumulated in the previous iteration of the evolution is utilized to forecast area where better optimization value can be located with the highest probability for the next searching operation. Five types of searching strategies are designed to accommodate different situations, which are determined by the history information. A suit of benchmark functions are used to test the SIF algorithm. The simulation results illustrate the good performance of SIF, especially for solving large scale optimization problems.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - Second International Conference, ICSI 2011
EditorsYing TAN, YuHui SHI, Yi CHAI, Guoyin WANG
Place of PublicationHeidelberg
PublisherSpringer Berlin
Pages374-381
Number of pages8
Volume6728
ISBN (Electronic)9783642215155
ISBN (Print)9783642215148
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2nd International Conference on Swarm Intelligence, ICSI 2011 - Chongqing, China
Duration: 12 Jun 201115 Jun 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
NumberPART 1
Volume6728
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Swarm Intelligence, ICSI 2011
Country/TerritoryChina
CityChongqing
Period12/06/1115/06/11

Keywords

  • Evolutionary Algorithm
  • Global Numerical Optimization
  • Search Interval Forecasting

Fingerprint

Dive into the research topics of 'A novel search interval forecasting optimization algorithm'. Together they form a unique fingerprint.

Cite this