A stochastic local search algorithm for constrained continuous global optimization

This paper presents a new stochastic local search algorithm known as feasible–infeasible search procedure (FISP) for constrained continuous global optimization. The proposed procedure uses metaheuristic strategies for combinatorial optimization as well as combined strategies for exploring continuous...

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Bibliographic Details
Published inInternational transactions in operational research Vol. 19; no. 6; pp. 825 - 846
Main Authors Melo, Wendel A.X., Fampa, Marcia H.C., Raupp, Fernanda M.P.
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.11.2012
Subjects
Algorithms
constrained continuous global optimization
metaheuristic
nonlinear optimization
Operations research
Optimization
Studies
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Summary:This paper presents a new stochastic local search algorithm known as feasible–infeasible search procedure (FISP) for constrained continuous global optimization. The proposed procedure uses metaheuristic strategies for combinatorial optimization as well as combined strategies for exploring continuous spaces, which are applied to an efficient process in increasingly refined neighborhoods of current points. We show effectiveness and efficiency of the proposed procedure on a standard set of 13 well‐known test problems. Furthermore, we compare the performance of FISP with SNOPT (sparse nonlinear optimizer) and with few successful existing stochastic algorithms on the same set of test problems.
Bibliography:ArticleID:ITOR854
istex:607EC671461C97B6D9EF86C9E849896C53B75CA9
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CNPq - No. 303097/2008-6; No. 307071/2008-1
FAPERJ/CNPq through PRONEX-Optimization
ISSN:0969-6016
1475-3995
DOI:10.1111/j.1475-3995.2012.00854.x