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A stochastic local search algorithm for constrained continuous global optimization
Author(s) -
Melo Wendel A.X.,
Fampa Marcia H.C.,
Raupp Fernanda M.P.
Publication year - 2012
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/j.1475-3995.2012.00854.x
Subject(s) - mathematical optimization , metaheuristic , set (abstract data type) , local search (optimization) , computer science , stochastic optimization , global optimization , continuous optimization , algorithm , optimization problem , mathematics , multi swarm optimization , programming language
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.