Adaptive differential evolution algorithm and its application to parameter estimation

Differential Evolution algorithm with Control Parameter Adaptation and Strategy Adaptation(DE-CPASA) is here introduced to solve the problem of parameter estimation. In DE-CPASA, differential evolution operator is used to search the optimization results of problems, and Gaussian distribution is employed to implement the adaptive control parameters. The strategy adaptation is achieved by the evaluation of fitness function. Simulation test results show that DE-CPASA can obtain more precision solution and have faster convergence. DE-CPASA is employed to estimate the kinetic parameters of Hg oxidation, and an optimization result is obtained.

[1]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[2]  Harvey G. Stenger,et al.  Development of a predictive kinetic model for homogeneous Hg oxidation data , 2007, Math. Comput. Model..

[3]  Janez Brest,et al.  Performance comparison of self-adaptive and adaptive differential evolution algorithms , 2007, Soft Comput..

[4]  Harvey G. Stenger,et al.  Comparing and interpreting laboratory results of Hg oxidation by a chlorine species , 2007 .

[5]  F. Scala,et al.  Mercury emissions from coal combustion: modeling and comparison of Hg capture in a fabric filter versus an electrostatic precipitator. , 2008, Journal of hazardous materials.

[6]  S. Ito,et al.  Mercury oxidation by copper oxides in combustion flue gases , 2008 .

[7]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[8]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[9]  Arthur C. Sanderson,et al.  Adaptive Differential Evolution , 2009 .

[10]  Xuefeng Yan,et al.  An Immune Self-adaptive Differential Evolution Algorithm with Application to Estimate Kinetic Parameters for Homogeneous Mercury Oxidation , 2009 .

[11]  Liu Yang.  Multi-agent differential evolution algorithm , 2011 .

[12]  K. He,et al.  Deactivation performance and mechanism of alkali (earth) metals on V2O5–WO3/TiO2 catalyst for oxidation of gaseous elemental mercury in simulated coal-fired flue gas , 2011 .

[13]  Leandro dos Santos Coelho,et al.  Solution of Jiles-Atherton vector hysteresis parameters estimation by modified Differential Evolution approaches , 2012, Expert Syst. Appl..

[14]  Adam P. Piotrowski,et al.  Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers , 2012, Expert Syst. Appl..

[15]  Sašo Džeroski,et al.  Parameter estimation in a nonlinear dynamic model of an aquatic ecosystem with meta-heuristic optimization , 2012 .

[16]  Danwei Wang,et al.  Fault detection, isolation and identification for hybrid systems with unknown mode changes and fault patterns , 2012, Expert Syst. Appl..

[17]  Chao Zhao,et al.  Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems , 2013 .

[18]  Samir Sayah,et al.  A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems , 2013, Appl. Soft Comput..

[19]  Antonin Ponsich,et al.  A hybrid Differential Evolution - Tabu Search algorithm for the solution of Job-Shop Scheduling Problems , 2013, Appl. Soft Comput..