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A stochastic algorithm for function minimization

SU DONGCAI(suntree4152***at***gmail.com)
DONG JUNWEI(djwei***at***vt.edu)
ZHENG ZUDUO(zhengzuduo***at***gmail.com)

Abstract: Focusing on what an optimization problem may comply with, the so-called convergence conditions have been proposed and sequentially a stochastic optimization algorithm named as DSZ algorithm is presented in order to deal with both unconstrained and constrained optimizations. Its principle is discussed in the theoretical model of DSZ algorithm, from which we present a practical model of DSZ algorithm. Practical modelís efficiency is demonstrated by comparison with similar algorithms such as Enhanced Simulated Annealing (ESA), Monte Carlo Simulated Annealing (MCS), Sniffer Global Optimization (SGO), Directed Tabu Search (DTS), and Genetic Algorithm (GA), using a set of well-known both unconstrained and constrained optimization test cases. Meanwhile, further attention goes to the strategy how to optimize the high-dimensional unconstrained problems using DSZ algorithm.

Keywords: Global optimization, unconstrained optimization, constrained optimization

Category 1: Global Optimization (Stochastic Approaches )

Category 2: Nonlinear Optimization (Unconstrained Optimization )

Category 3: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Submitted to Journal of heuristic on July 1,2008

Download: [PDF]

Entry Submitted: 07/06/2008
Entry Accepted: 07/07/2008
Entry Last Modified: 07/06/2008

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