A novel search interval forecasting optimization algorithm

Yang Lou; Junli Li; Yuhui Shi; Linpeng Jin

doi:10.1007/978-3-642-21515-5_44

A novel search interval forecasting optimization algorithm

Yang Lou^*
, Junli Li
, Yuhui Shi
, Linpeng Jin

^*Corresponding author for this work

Ningbo University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-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 language	English
Title of host publication	Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Proceedings
Pages	374-381
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-21515-5_44
Publication status	Published - 2011
Event	2nd International Conference on Swarm Intelligence, ICSI 2011 - Chongqing, China Duration: 12 Jun 2011 → 15 Jun 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6728 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Swarm Intelligence, ICSI 2011
Country/Territory	China
City	Chongqing
Period	12/06/11 → 15/06/11

Keywords

Evolutionary Algorithm
Global Numerical Optimization
Search Interval Forecasting

Access to Document

10.1007/978-3-642-21515-5_44

Cite this

Lou, Y., Li, J., Shi, Y., & Jin, L. (2011). A novel search interval forecasting optimization algorithm. In Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Proceedings (PART 1 ed., pp. 374-381). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6728 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-21515-5_44

@inproceedings{7c42d7bac2774355a8c39b9b9531fda8,

title = "A novel search interval forecasting optimization algorithm",

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.",

keywords = "Evolutionary Algorithm, Global Numerical Optimization, Search Interval Forecasting",

author = "Yang Lou and Junli Li and Yuhui Shi and Linpeng Jin",

year = "2011",

doi = "10.1007/978-3-642-21515-5\_44",

language = "English",

isbn = "9783642215148",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 1",

pages = "374--381",

booktitle = "Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Proceedings",

edition = "PART 1",

note = "2nd International Conference on Swarm Intelligence, ICSI 2011 ; Conference date: 12-06-2011 Through 15-06-2011",

}

Lou, Y, Li, J, Shi, Y & Jin, L 2011, A novel search interval forecasting optimization algorithm. in Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6728 LNCS, pp. 374-381, 2nd International Conference on Swarm Intelligence, ICSI 2011, Chongqing, China, 12/06/11. https://doi.org/10.1007/978-3-642-21515-5_44

A novel search interval forecasting optimization algorithm. / Lou, Yang; Li, Junli; Shi, Yuhui et al.
Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Proceedings. PART 1. ed. 2011. p. 374-381 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6728 LNCS, No. PART 1).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - A novel search interval forecasting optimization algorithm

AU - Lou, Yang

AU - Li, Junli

AU - Shi, Yuhui

AU - Jin, Linpeng

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - Evolutionary Algorithm

KW - Global Numerical Optimization

KW - Search Interval Forecasting

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U2 - 10.1007/978-3-642-21515-5_44

DO - 10.1007/978-3-642-21515-5_44

M3 - Conference Proceeding

AN - SCOPUS:79958238524

SN - 9783642215148

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Proceedings

T2 - 2nd International Conference on Swarm Intelligence, ICSI 2011

Y2 - 12 June 2011 through 15 June 2011

ER -

Lou Y, Li J, Shi Y, Jin L. A novel search interval forecasting optimization algorithm. In Advances in Swarm Intelligence - Second International Conference, ICSI 2011, Proceedings. PART 1 ed. 2011. p. 374-381. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-21515-5_44

A novel search interval forecasting optimization algorithm

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