Free Search in Multidimensional Space III

Research output: Chapter in Book/Report/Published conference proceedingChapter

Abstract

Abstract. Various scientific and technological fields, such as design, engineering, physics, chemistry, economics, business, and finance often face multidimensional optimisation problems. Although substantial research efforts have been directed in this area, key questions are still waiting for answers, such as: What limits computer aided design systems on optimisation tasks with high variables number? How to improve capabilities of modern search methods applied to multidimensional problems? What are software and hardware constraints? Approaching multidimensional optimisation problems raises in addition new research questions, which cannot be seen or identified on low dimensional tasks, such as: What time is required to re-solve multidimensional task with acceptable level of precision? How dimensionality reflects on the search space complexity? How to establish search process orientation, within multidimensional space? How task specific landscapes embarrass orientation? This article presents an investigation on 300 dimensional heterogeneous real-value numerical tests. The study aims to evaluate relation between tasks’ dimensions’ number and required for achieving acceptable solution with non-zero probability number of objective function evaluations. Experimental results are presented, analysed and com-pared to other publications.
Original languageEnglish
Title of host publication Lecture Notes in Computer Science
EditorsIvan Lirkov, Svetozar Margenov, Jerzy Waśniewski
Place of PublicationUS
PublisherSpringer Nature
Pages251
Number of pages257
Volume9374
Edition1
ISBN (Electronic)978-3-319-26520-9
ISBN (Print)978-3-319-26519-3
DOIs
Publication statusPublished - 29 Nov 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Cham
Number1
Volume9374
ISSN (Print)0302-9743

Fingerprint

Function evaluation
Finance
Computer aided design
Optimization Problem
Physics
Space Complexity
Hardware
Computer-aided Design
Engineering Design
Economics
Search Methods
Search Space
Chemistry
Dimensionality
Objective function
Industry
Software
Optimization
Evaluate
Evaluation

Cite this

Penev, K. (2015). Free Search in Multidimensional Space III. In I. Lirkov, S. Margenov, & J. Waśniewski (Eds.), Lecture Notes in Computer Science (1 ed., Vol. 9374, pp. 251). (Lecture Notes in Computer Science; Vol. 9374, No. 1). US: Springer Nature. https://doi.org/10.1007/978-3-319-26520-9_27
Penev, Kalin. / Free Search in Multidimensional Space III. Lecture Notes in Computer Science. editor / Ivan Lirkov ; Svetozar Margenov ; Jerzy Waśniewski. Vol. 9374 1. ed. US : Springer Nature, 2015. pp. 251 (Lecture Notes in Computer Science; 1).
@inbook{f2582337fcf64fd89233068163d0d5e9,
title = "Free Search in Multidimensional Space III",
abstract = "Abstract. Various scientific and technological fields, such as design, engineering, physics, chemistry, economics, business, and finance often face multidimensional optimisation problems. Although substantial research efforts have been directed in this area, key questions are still waiting for answers, such as: What limits computer aided design systems on optimisation tasks with high variables number? How to improve capabilities of modern search methods applied to multidimensional problems? What are software and hardware constraints? Approaching multidimensional optimisation problems raises in addition new research questions, which cannot be seen or identified on low dimensional tasks, such as: What time is required to re-solve multidimensional task with acceptable level of precision? How dimensionality reflects on the search space complexity? How to establish search process orientation, within multidimensional space? How task specific landscapes embarrass orientation? This article presents an investigation on 300 dimensional heterogeneous real-value numerical tests. The study aims to evaluate relation between tasks’ dimensions’ number and required for achieving acceptable solution with non-zero probability number of objective function evaluations. Experimental results are presented, analysed and com-pared to other publications.",
author = "Kalin Penev",
year = "2015",
month = "11",
day = "29",
doi = "10.1007/978-3-319-26520-9_27",
language = "English",
isbn = "978-3-319-26519-3",
volume = "9374",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
number = "1",
pages = "251",
editor = "{ Lirkov}, Ivan and Margenov, {Svetozar } and Waśniewski, {Jerzy }",
booktitle = "Lecture Notes in Computer Science",
address = "United States",
edition = "1",

}

Penev, K 2015, Free Search in Multidimensional Space III. in I Lirkov, S Margenov & J Waśniewski (eds), Lecture Notes in Computer Science. 1 edn, vol. 9374, Lecture Notes in Computer Science, no. 1, vol. 9374, Springer Nature, US, pp. 251. https://doi.org/10.1007/978-3-319-26520-9_27

Free Search in Multidimensional Space III. / Penev, Kalin.

Lecture Notes in Computer Science. ed. / Ivan Lirkov; Svetozar Margenov; Jerzy Waśniewski. Vol. 9374 1. ed. US : Springer Nature, 2015. p. 251 (Lecture Notes in Computer Science; Vol. 9374, No. 1).

Research output: Chapter in Book/Report/Published conference proceedingChapter

TY - CHAP

T1 - Free Search in Multidimensional Space III

AU - Penev, Kalin

PY - 2015/11/29

Y1 - 2015/11/29

N2 - Abstract. Various scientific and technological fields, such as design, engineering, physics, chemistry, economics, business, and finance often face multidimensional optimisation problems. Although substantial research efforts have been directed in this area, key questions are still waiting for answers, such as: What limits computer aided design systems on optimisation tasks with high variables number? How to improve capabilities of modern search methods applied to multidimensional problems? What are software and hardware constraints? Approaching multidimensional optimisation problems raises in addition new research questions, which cannot be seen or identified on low dimensional tasks, such as: What time is required to re-solve multidimensional task with acceptable level of precision? How dimensionality reflects on the search space complexity? How to establish search process orientation, within multidimensional space? How task specific landscapes embarrass orientation? This article presents an investigation on 300 dimensional heterogeneous real-value numerical tests. The study aims to evaluate relation between tasks’ dimensions’ number and required for achieving acceptable solution with non-zero probability number of objective function evaluations. Experimental results are presented, analysed and com-pared to other publications.

AB - Abstract. Various scientific and technological fields, such as design, engineering, physics, chemistry, economics, business, and finance often face multidimensional optimisation problems. Although substantial research efforts have been directed in this area, key questions are still waiting for answers, such as: What limits computer aided design systems on optimisation tasks with high variables number? How to improve capabilities of modern search methods applied to multidimensional problems? What are software and hardware constraints? Approaching multidimensional optimisation problems raises in addition new research questions, which cannot be seen or identified on low dimensional tasks, such as: What time is required to re-solve multidimensional task with acceptable level of precision? How dimensionality reflects on the search space complexity? How to establish search process orientation, within multidimensional space? How task specific landscapes embarrass orientation? This article presents an investigation on 300 dimensional heterogeneous real-value numerical tests. The study aims to evaluate relation between tasks’ dimensions’ number and required for achieving acceptable solution with non-zero probability number of objective function evaluations. Experimental results are presented, analysed and com-pared to other publications.

UR - https://link.springer.com/chapter/10.1007/978-3-319-26520-9_27

U2 - 10.1007/978-3-319-26520-9_27

DO - 10.1007/978-3-319-26520-9_27

M3 - Chapter

SN - 978-3-319-26519-3

VL - 9374

T3 - Lecture Notes in Computer Science

SP - 251

BT - Lecture Notes in Computer Science

A2 - Lirkov, Ivan

A2 - Margenov, Svetozar

A2 - Waśniewski, Jerzy

PB - Springer Nature

CY - US

ER -

Penev K. Free Search in Multidimensional Space III. In Lirkov I, Margenov S, Waśniewski J, editors, Lecture Notes in Computer Science. 1 ed. Vol. 9374. US: Springer Nature. 2015. p. 251. (Lecture Notes in Computer Science; 1). https://doi.org/10.1007/978-3-319-26520-9_27