Differential Evolution with Tabu List for Global Optimization and Its Application to Phase Equilibrium and Parameter Estimation Problems

详细信息    查看全文

• 作者：Mekapati Srinivas ; G. P. Rangaiah
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2007
• 出版时间：May 9, 2007
• 年：2007
• 卷：46
• 期：10
• 页码：3410 - 3421
• 全文大小：245K
• 年卷期：v.46,no.10(May 9, 2007)
• ISSN：1520-5045

文摘

Stochastic global optimization and their applications are attracting greater attention and interest in the recentpast as they provide better solutions with relatively less computational effort. Among the many popular methods,differential evolution (DE), proposed by Storn and Price [J. Global Optim. 1997, 11, 341-359], is a population-based direct search algorithm for nonlinear and nondifferentiable functions, and has found numerous applicationsdue its simplicity, ease of use, and faster convergence. In this work, we attempted to improve the computationalefficiency of DE further by implementing the concept (i.e., avoiding revisits during the search) of tabu search(TS) using the tabu list in the generation step of DE; it also provides diversity among the members of thepopulation. DE with tabu list (DETL) is initially tested on several benchmark problems involving a few tothousands of local minima and 2-20 variables. It is then tested on challenging phase equilibrium calculationsfollowed by parameter estimation problems in dynamic systems known to have multiple minima. The resultsshow that the performance of DETL is better compared to DE and TS for benchmark, phase equilibrium, andparameter estimation problems.