Adekanmbi Oluwole, Green Paul
Department of Finance and Information Management, Durban University of Technology, P.O. Box 101112, Scottsville, Pietermaritzburg 3209, South Africa.
ScientificWorldJournal. 2015;2015:936106. doi: 10.1155/2015/936106. Epub 2015 Mar 19.
Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineering problems. GDE3 metaheuristic modifies the selection process of the basic differential evolution and extends DE/rand/1/bin strategy in solving practical applications. The performance of the metaheuristic is investigated through engineering design optimization problems and the results are reported. The comparison of the numerical results with those of other metaheuristic techniques demonstrates the promising performance of the algorithm as a robust optimization tool for practical purposes.
元启发式算法是众所周知的优化工具,已被用于解决各种优化问题。差分进化的几种扩展方法已被用于解决约束和无约束多目标优化问题,但在本研究中,广义差分进化(GDE)的第三个版本被用于解决实际工程问题。GDE3元启发式算法修改了基本差分进化的选择过程,并在解决实际应用中扩展了DE/rand/1/bin策略。通过工程设计优化问题研究了该元启发式算法的性能,并报告了结果。将数值结果与其他元启发式技术的结果进行比较,证明了该算法作为一种用于实际目的的鲁棒优化工具具有良好的性能。