Download PDF by Jingqiao Zhang, Arthur C. Sanderson: Adaptive differential evolution: a robust approach to

By Jingqiao Zhang, Arthur C. Sanderson

ISBN-10: 3642015263

ISBN-13: 9783642015267

Optimization difficulties are ubiquitous in educational examine and real-world functions at any place such assets as house, time and value are restricted. Researchers and practitioners have to remedy difficulties basic to their day-by-day paintings which, notwithstanding, may perhaps express a number of demanding features reminiscent of discontinuity, nonlinearity, nonconvexity, and multimodality. it really is anticipated that fixing a posh optimization challenge itself should still effortless to take advantage of, trustworthy and effective to accomplish passable solutions.

Differential evolution is a contemporary department of evolutionary algorithms that's in a position to addressing a large set of advanced optimization difficulties in a comparatively uniform and conceptually basic demeanour. For greater functionality, the regulate parameters of differential evolution have to be set adequately as they've got diverse results on evolutionary seek behaviours for varied difficulties or at assorted optimization levels of a unmarried challenge. the basic subject of the e-book is theoretical learn of differential evolution and algorithmic research of parameter adaptive schemes. subject matters lined during this booklet include:

  • Theoretical research of differential evolution and its keep watch over parameters
  • Algorithmic layout and comparative research of parameter adaptive schemes
  • Scalability research of adaptive differential evolution
  • Adaptive differential evolution for multi-objective optimization
  • Incorporation of surrogate version for computationally pricey optimization
  • Application to winner choice in combinatorial auctions of E-Commerce
  • Application to flight course making plans in Air site visitors Management
  • Application to transition likelihood matrix optimization in credit-decision making

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Extra resources for Adaptive differential evolution: a robust approach to multimodal problem optimization

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The problem, however, is how to design a simple yet effective scheme to adapt the mutation factor in different evolution stages. 6 Summary In this chapter, we have proposed an analytical method to investigate the evolutionary stochastic properties of DE on the sphere model. Resorting to the geometric symmetry of the sphere model, we have identified the property of rotational invariance which is satisfied by the parental and intermediate populations at each generation. This property is utilized to introduce an approximate model of DE, based on which the analysis of the evolutionary dynamics of DE can be done in a mathematical manner.

All these adaptive DE algorithms, except JADE, adopt DE/rand/1 as the underlying mutation strategy or one of the two strategies, mainly because it is usually considered less greedy and more robust than other mutation variants. JADE is the only algorithm to implement a relatively greedy mutation strategy which utilizes the information of both the high-quality solutions in the current population and the inferior solutions previously explored during the optimization search. It is expected that a greedy mutation strategy and a parameter adaptation scheme can be mutually beneficial, since the former is capable of increasing the convergence rate while the latter is helpful to maintain the reliability of the algorithm at a high level by adapting to appropriate control parameter values.

5 (these are the two typical values used in the literature). Then, systematic experiments are conducted for a large range of D and NP to identify the applicability and the limitation of the analytical model. 1 Performance Metrics The progress rate φ R is a central quantity which has been used in the literature [11], [12] to characterize the effectiveness of an evolutionary algorithm. , φR,g = Rg − Rg+1. In view of its R-independent and D-dependent property, a normalized progress rate is introduced as follows: D ∗ φR,g = φR,g .

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Adaptive differential evolution: a robust approach to multimodal problem optimization by Jingqiao Zhang, Arthur C. Sanderson


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