Neurodynamic differential evolution algorithm and solving CEC2015 competition problems

Recently, the success history based parameter adaptation for differential evolution algorithm with linear population size reduction has been claimed to be a great algorithm for solving optimization problems. Neuro-dynamic is another recent approach that has shown remarkable convergence for certain problems, even for high dimensional cases. In this paper, we proposed a new algorithm by embedding the concept of neuro-dynamic into a modified success history based parameter adaptation for differential evolution with linear population size reduction. We have also proposed an adaptive mechanism for the appropriate use of the success history based parameter adaptation for differential evolution with linear population size reduction and neuro-dynamic during the search process. The new algorithm has been tested on the CEC'2015 single objective real-parameter competition problems. The experimental results show that the proposed algorithm is capable of producing good solutions that are clearly better than those obtained from the success history based parameter adaptation for differential evolution with linear population size reduction and a few of the other state-of-the-art algorithms considered in this paper.

[1]  Leon O. Chua,et al.  Neural networks for nonlinear programming , 1988 .

[2]  Kalyanmoy Deb,et al.  Optimization for Engineering Design: Algorithms and Examples , 2004 .

[3]  Ruhul A. Sarker,et al.  United multi-operator evolutionary algorithms , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[4]  Ruhul A. Sarker,et al.  Differential evolution with multiple strategies for solving CEC2011 real-world numerical optimization problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[5]  Linda R. Petzold,et al.  Numerical solution of initial-value problems in differential-algebraic equations , 1996, Classics in applied mathematics.

[6]  R. Glynn,et al.  The Wilcoxon Signed Rank Test for Paired Comparisons of Clustered Data , 2006, Biometrics.

[7]  Alex S. Fukunaga,et al.  Improving the search performance of SHADE using linear population size reduction , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[8]  Ruhul A. Sarker,et al.  Multi-operator based evolutionary algorithms for solving constrained optimization problems , 2011, Comput. Oper. Res..

[9]  Yee Leung,et al.  A high-performance feedback neural network for solving convex nonlinear programming problems , 2003, IEEE Trans. Neural Networks.

[10]  Charles S. Newton,et al.  Evolutionary Optimization (Evopt): A Brief Review And Analysis , 2003, Int. J. Comput. Intell. Appl..

[11]  Jun Wang,et al.  A general projection neural network for solving monotone variational inequalities and related optimization problems , 2004, IEEE Transactions on Neural Networks.

[12]  John J. Hopfield,et al.  Simple 'neural' optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit , 1986 .

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

[14]  Alex S. Fukunaga,et al.  Evaluating the performance of SHADE on CEC 2013 benchmark problems , 2013, 2013 IEEE Congress on Evolutionary Computation.

[15]  D. Fogel.  Evolutionary algorithms in theory and practice , 1997, Complex..

[16]  Jun Wang,et al.  A collective neurodynamic optimization approach to bound-constrained nonconvex optimization , 2014, Neural Networks.

[17]  Jun Wang,et al.  A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints , 2000, IEEE Trans. Neural Networks Learn. Syst..

[18]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[19]  Jiahai Wang,et al.  Combining tabu Hopfield network and estimation of distribution for unconstrained binary quadratic programming problem , 2011, Expert Syst. Appl..

[20]  Stefen Hui,et al.  Neural networks for constrained optimization problems , 1993, Int. J. Circuit Theory Appl..

[21]  Liqun Qi,et al.  Neurodynamical Optimization , 2004, J. Glob. Optim..

[22]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[23]  Jun Wang,et al.  A recurrent neural network for solving nonlinear convex programs subject to linear constraints , 2005, IEEE Transactions on Neural Networks.

[24]  Youshen Xia,et al.  A new neural network for solving linear and quadratic programming problems , 1996, IEEE Trans. Neural Networks.

[25]  Leszek Rutkowski,et al.  Computational intelligence - methods and techniques , 2008 .

[26]  Ruhul A. Sarker,et al.  Training and testing a self-adaptive multi-operator evolutionary algorithm for constrained optimization , 2015, Appl. Soft Comput..

[27]  Edgar Sanchez-Sinencio,et al.  Nonlinear switched capacitor 'neural' networks for optimization problems , 1990 .

[28]  Michael A. Shanblatt,et al.  A two-phase optimization neural network , 1992, IEEE Trans. Neural Networks.

[29]  Ruhul A. Sarker,et al.  Memetic multi-topology particle swarm optimizer for constrained optimization , 2012, 2012 IEEE Congress on Evolutionary Computation.