Far East Journal of Experimental and Theoretical Artificial Intelligence
Volume 3, Issue 2 (Special Issue on 13th Conference on Artificial Intelligence and Applications (TAAI 2008), Pages 143 - 168
(May 2009)
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ADVANCED SIMULATED ANNEALING FOR SOLVING CONSTRAINED GLOBAL OPTIMIZATION PROBLEMS
Jui-Yu Wu (Taiwan)
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Abstract:
Global optimization can be divided into two branches, deterministic and stochastic optimizations. Deterministic global optimization approaches often involve a sophisticated optimization process, and usually make some assumptions regarding the problem to be solved. These methods can be computationally tedious and difficult for general practitioners to use. To overcome these limitations, this study presents an advanced simulated annealing (ASA) algorithm (stochastic optimization method) based on the penalty function for solving a multi-dimensional function with bounded and constrained search spaces. Performance of the proposed ASA algorithm is measured by using a set of constrained global optimization (CGO) problems, including benchmark nonlinear programming problems and generalized polynomial programming problems with a nonconvex objective function and nonlinear constraints. Numerical results show that the ASA algorithm can converge to a global solution for a CGO problem. |
| Keywords and phrases: global optimization, simulated annealing, nonlinear programming. |
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