Unidimensional Search for Solving Continuous High-Dimensional Optimization Problems

This paper presents a performance study of two versions of a unidimensional search algorithm aimed at solving high-dimensional optimization problems. The algorithms were tested on 11 scalable benchmark problems. The aim is to observe how metaheuristics for continuous optimization problems respond wi...

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Bibliographic Details
Published in2009 Ninth International Conference on Intelligent Systems Design and Applications pp. 1096 - 1101
Main Authors Gardeux, V., Chelouah, R., Siarry, P., Glover, F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
Subjects
Algorithm design and analysis
Benchmark testing
Convergence
Design optimization
Evolutionary computation
Greedy algorithms
Humans
Intelligent systems
metaheuristic
optimization
Robustness
unidimensional
Web mining
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ISBN1424447356
9781424447350
ISSN2164-7143
DOI10.1109/ISDA.2009.191

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Summary:This paper presents a performance study of two versions of a unidimensional search algorithm aimed at solving high-dimensional optimization problems. The algorithms were tested on 11 scalable benchmark problems. The aim is to observe how metaheuristics for continuous optimization problems respond with increasing dimension. To this end, we report the algorithms' performance on the 50, 100, 200 and 500-dimension versions of each function. Computational results are given along with convergence graphs to provide comparisons with other algorithms during the conference and afterwards.
ISBN:1424447356
9781424447350
ISSN:2164-7143
DOI:10.1109/ISDA.2009.191