Solving Multi-Center Dynamic Optimization Problems Using Modified Differential Evolution

Xing Bao Liu; Jian Ping Yin

doi:10.4028/www.scientific.net/AMM.462-463.356

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 462-463Solving Multi-Center Dynamic Optimization Problems...

Solving Multi-Center Dynamic Optimization Problems Using Modified Differential Evolution

Abstract:

A novel self-learning differential evolution is proposed to solve multi-center optimization under dynamic environments. The approach of Re-evaluating is used to monitor environmental changes, then historial best individual obtained the environment guides population to new environment. What's more, the self-learning mechanism is employed to reduce the impact of dynamic changes of environment.The experimental results on a set of 4 test dynamic functions show that, self-learning differential algorithm outperforms other algorithms in term of the convergence speed.

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Periodical:

Applied Mechanics and Materials (Volumes 462-463)

Pages:

356-359

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.462-463.356

Citation:

Cite this paper

Online since:

November 2013

Authors:

Xing Bao Liu*, Jian Ping Yin

Keywords:

Artificial Intelligence (AI), Differential Evolution, Dynamic Environment, Intelligent Computation, Multi-Center Dynamic Optimization Problems

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