8 A Review of Major Application Areas of Differential Evolution <s> The Differential Evolution Algorithm <s> A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. ________________________________________ 1)International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1198, Suite 600, Fax: 510-643-7684. E-mail: storn@icsi.berkeley.edu. On leave from Siemens AG, ZFE T SN 2, OttoHahn-Ring 6, D-81739 Muenchen, Germany. Fax: 01149-636-44577, Email:rainer.storn@zfe.siemens.de. 2)836 Owl Circle, Vacaville, CA 95687, kprice@solano.community.net. <s> BIB001 </s> 8 A Review of Major Application Areas of Differential Evolution <s> The Differential Evolution Algorithm <s> A simple optimization procedure for constraint-based problems is described which works with a simplified cost function or even without one. The simplification of the problem formulation makes this method particularly attractive. The new method lends itself to parallel computation and is well suited for constraint satisfaction, constrained optimization, and design centering problems. A further asset is its self-steering property which makes the new method easy to use. <s> BIB002
More than ten years ago, Storn and Price BIB001 have presented a novel optimization method, called Differential Evolution (DE), which has been designed to handle nondifferentiable, nonlinear and multimodal objective functions. To fulfill this requirement, DE has been designed as a stochastic parallel direct search method, which utilizes concepts borrowed from the broad class of evolutionary algorithms, but requires few easily chosen control parameters. Early experimental results have shown that DE has good convergence properties and outperforms other well known evolutionary algorithms BIB002 BIB001 . In the following paragraphs we outline the workings of DE.