Abstract
Differential evolution (DE), a population-based direct-search algorithm, has been gaining popularity in the recent past due to its simplicity and ability to handle nonlinear, nondifferentiable, and nonconvex functions. In this study, a method, namely, differential evolution with tabu list (DETL), is described and evaluated for solving constrained optimization problems encountered in chemical engineering. It incorporates the concept of tabu search (TS) (i.e., avoiding revisits during the search) in DE mainly to improve its computational efficiency. DETL is initially applied to many nonlinear programming problems (NLPs) involving 2−13 variables and up to 38 constraints. It is then tested on several mixed-integer nonlinear programming problems (MINLPs) encountered in chemical engineering practice. The performance results of DETL, DE, and modified differential evolution (MDE) (Babu, K. V.; Angira, R. Comput. Chem. Eng. 2006, 30, 989), for both NLPs and MINLPs, are presented, and the relative performance of the three methods is discussed.
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