A hybrid heuristic method for global optimization

Antoniya Georgieva; Ivan Jordanov

doi:10.1109/ICHIS.2005.11

A hybrid heuristic method for global optimization

Antoniya Georgieva^*, Ivan Jordanov

^*Corresponding author for this work

School of Computing

Computer Scicnce and Software Engineering Department

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper a new stochastic hybrid technique for unconstrained global optimization (GO) is proposed. It is a combination of an iterative algorithm developed by us (called LP_τ O) that uses low-discrepancy sequences of points and heuristic knowledge to find regions of attraction when searching for a global minimum (GM) and the well-known Nelder-Mead Simplex Local Search. The combination of the two techniques provides a powerful hybrid optimization tool that we call LP_τSS. The proposed LP _τSS method is tested on a number of multimodal mathematical functions and results are discussed and compared with such from other stochastic methods.

Original language	English
Title of host publication	Fifth International Conference on Hybrid Intelligent Systems (HIS'05)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	503-505
Number of pages	3
ISBN (Print)	0769524575, 9780769524573
DOIs	https://doi.org/10.1109/ICHIS.2005.11
Publication status	Published - 6 Feb 2006
Event	HIS 2005: Fifth International Conference on Hybrid Intelligent Systems - Rio de Janiero, Brazil Duration: 6 Nov 2005 → 9 Nov 2005

Conference

Conference	HIS 2005: Fifth International Conference on Hybrid Intelligent Systems
Country/Territory	Brazil
City	Rio de Janiero
Period	6/11/05 → 9/11/05

Keywords

Global optimization
Heuristics
Hybrid methods
Low-discrepancy sequences
Simplex search

Access to Document

10.1109/ICHIS.2005.11

Cite this

@inproceedings{ac20edd0164d4007aea6a7dca8b2cbe9,

title = "A hybrid heuristic method for global optimization",

abstract = "In this paper a new stochastic hybrid technique for unconstrained global optimization (GO) is proposed. It is a combination of an iterative algorithm developed by us (called LPτ O) that uses low-discrepancy sequences of points and heuristic knowledge to find regions of attraction when searching for a global minimum (GM) and the well-known Nelder-Mead Simplex Local Search. The combination of the two techniques provides a powerful hybrid optimization tool that we call LPτSS. The proposed LP τSS method is tested on a number of multimodal mathematical functions and results are discussed and compared with such from other stochastic methods.",

keywords = "Global optimization, Heuristics, Hybrid methods, Low-discrepancy sequences, Simplex search",

author = "Antoniya Georgieva and Ivan Jordanov",

year = "2006",

month = feb,

day = "6",

doi = "10.1109/ICHIS.2005.11",

language = "English",

isbn = "0769524575",

pages = "503--505",

booktitle = "Fifth International Conference on Hybrid Intelligent Systems (HIS'05)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "HIS 2005: Fifth International Conference on Hybrid Intelligent Systems ; Conference date: 06-11-2005 Through 09-11-2005",

}

TY - GEN

T1 - A hybrid heuristic method for global optimization

AU - Georgieva, Antoniya

AU - Jordanov, Ivan

PY - 2006/2/6

Y1 - 2006/2/6

N2 - In this paper a new stochastic hybrid technique for unconstrained global optimization (GO) is proposed. It is a combination of an iterative algorithm developed by us (called LPτ O) that uses low-discrepancy sequences of points and heuristic knowledge to find regions of attraction when searching for a global minimum (GM) and the well-known Nelder-Mead Simplex Local Search. The combination of the two techniques provides a powerful hybrid optimization tool that we call LPτSS. The proposed LP τSS method is tested on a number of multimodal mathematical functions and results are discussed and compared with such from other stochastic methods.

AB - In this paper a new stochastic hybrid technique for unconstrained global optimization (GO) is proposed. It is a combination of an iterative algorithm developed by us (called LPτ O) that uses low-discrepancy sequences of points and heuristic knowledge to find regions of attraction when searching for a global minimum (GM) and the well-known Nelder-Mead Simplex Local Search. The combination of the two techniques provides a powerful hybrid optimization tool that we call LPτSS. The proposed LP τSS method is tested on a number of multimodal mathematical functions and results are discussed and compared with such from other stochastic methods.

KW - Global optimization

KW - Heuristics

KW - Hybrid methods

KW - Low-discrepancy sequences

KW - Simplex search

UR - https://www.scopus.com/pages/publications/33846983588

U2 - 10.1109/ICHIS.2005.11

DO - 10.1109/ICHIS.2005.11

M3 - Conference contribution

AN - SCOPUS:33846983588

SN - 0769524575

SN - 9780769524573

SP - 503

EP - 505

BT - Fifth International Conference on Hybrid Intelligent Systems (HIS'05)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - HIS 2005: Fifth International Conference on Hybrid Intelligent Systems

Y2 - 6 November 2005 through 9 November 2005

ER -

A hybrid heuristic method for global optimization

Abstract

Conference

Keywords

Access to Document

Fingerprint

Cite this