By Rainer Storn (auth.), Uday K. Chakraborty (eds.)
Differential evolution is arguably one of many preferred themes in brand new computational intelligence examine. This e-book seeks to give a finished research of the state-of-the-art during this expertise and likewise instructions for destiny learn.
The fourteen chapters of this publication were written by way of prime specialists within the sector. the 1st seven chapters concentrate on set of rules layout, whereas the final seven describe real-world purposes. bankruptcy 1 introduces the fundamental differential evolution (DE) set of rules and offers a large assessment of the sphere. bankruptcy 2 offers a brand new, rotationally invariant DE set of rules. The function of self-adaptive keep an eye on parameters in DE is investigated in bankruptcy three. Chapters four and five handle limited optimization; the previous develops appropriate preventing stipulations for the DE run, and the latter offers a better DE set of rules for issues of very small possible areas. a singular DE set of rules, according to the concept that of "opposite" issues, is the subject of bankruptcy 6. bankruptcy 7 presents a survey of multi-objective differential evolution algorithms. A evaluation of the key program parts of differential evolution is gifted in bankruptcy eight. bankruptcy nine discusses the applying of differential evolution in vital components of utilized electromagnetics. Chapters 10 and eleven concentrate on purposes of hybrid DE algorithms to difficulties in energy process optimization. bankruptcy 12 applies the DE set of rules to machine chess. using DE to resolve an issue in bioprocess engineering is mentioned in bankruptcy thirteen. bankruptcy 14 describes the appliance of hybrid differential evolution to an issue up to the mark engineering.
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Extra info for Advances in differential evolution
Multi-objective parameter estimation problems of fermentation processes using a high ethanol tolerance yeast. : PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the 2001 congress on evolutionary computation, vol. 2, pp. 971–978. : The self-adaptive Pareto differential evolution algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii, May 2002, pp. : Multiobjective optimization using a Pareto Differential Evolution approach.
PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the 2001 congress on evolutionary computation, vol. 2, pp. 971–978. : The self-adaptive Pareto differential evolution algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii, May 2002, pp. : Multiobjective optimization using a Pareto Differential Evolution approach. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), Honolulu, Hawaii, May 2002, pp.
For a specific application it may be worthwhile to revisit certain variants of DE depending on the properties of the objective functions at hand. There are other potential possibilities to accelerate the convergence of DE-based digital filter design, one of which is using hybrid methods. In [62, 63, 64, 65] hybrid methods have already been used successfully. The basic idea usually is to refine one or more points from the DE-population by applying a fast-converging local search method like the Nelder and Mead optimization , dynamic hill climbing , or gradient type of algorithms [60, 67].
Advances in differential evolution by Rainer Storn (auth.), Uday K. Chakraborty (eds.)