Authors: Wali Khan Mashwani
Addresses: Department of Mathematics, Kohat University of Science and Technology, Kohat, Khyber Pakhtunkhwa (KPK), Pakistan
Abstract: Over the past few years, differential evolution (DE) is generally considered as a reliable, accurate and robust population-based evolutionary algorithm (EA). It is capable of handling non-differentiable, non-linear, multi-modal and constrained optimisation problems. However, it suffers from slow convergence rate and takes large computational time for optimising the computationally expensive objective functions including problems dimensionality, several local and global optimums. Over the last few years, several attempts have been made to overcome these drawbacks of simple DE by employing the key features of some existing evolutionary algorithms either self-adaptively and have been formed in the forms of enhanced versions of DEs. This paper reviews those efforts and gathered state-of-the-art survey of the DEs that included some novel self-adaptive mechanisms, different ensemble techniques, efficient local search optimisers and various constrained handling techniques.
Keywords: differential evolution; hybridisation; local search; constrained optmisation; evolutionary algorithms; self-adaptive mechanisms; ensemble techniques; constraint handling.
International Journal of Computing Science and Mathematics, 2014 Vol.5 No.2, pp.107 - 126
Available online: 31 Jul 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article