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New Tabu Search based global optimization methods outline of algorithms and study of efficiency
Author(s) -
Stepanenko Svetlana,
Engels Bernd
Publication year - 2007
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20830
Subject(s) - tabu search , maxima and minima , curse of dimensionality , mathematical optimization , gradient descent , convergence (economics) , global optimization , hill climbing , algorithm , computer science , guided local search , mathematics , artificial intelligence , artificial neural network , mathematical analysis , economics , economic growth
The study presents two new nonlinear global optimization routines; the Gradient Only Tabu Search (GOTS) and the Tabu Search with Powell's Algorithm (TSPA). They are based on the Tabu‐Search strategy, which tries to determine the global minimum of a function by the steepest descent–mildest ascent strategy. The new algorithms are explained and their efficiency is compared with other approaches by determining the global minima of various well‐known test functions with varying dimensionality. These tests show that for most tests the GOTS possesses a much faster convergence than global optimizer taken from the literature. The efficiency of the TSPA compares to the efficiency of genetic algorithms. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2008