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Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Authors: Rainer Storn, Kenneth Price

Published: 1997 (Journal Paper)

Source: Journal of Global Optimization

Algorithm: Differential Evolution

DOI: 10.1023/A:1008202821328

Summary

Introduces Differential Evolution, a population-based global optimizer that mutates candidate vectors by scaled differences between other candidates and then applies crossover and selection. Its practical appeal comes from a small number of control parameters, simple implementation, and strong performance on continuous black-box optimization problems.

Abstract

A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.

Primary

Paper doi.org

Standard

DOI 10.1023/A:1008202821328

Alternate

link.springer.com link.springer.com researchgate.net researchgate.net

Tags

  • Differential evolution

  • Evolutionary computation

  • Global optimization

  • Continuous optimization

  • Derivative-free optimization

  • Stochastic optimization

  • Population-based search