An improved differential evolution algorithm with novel mutation strategy

As a modern Evolutionary Algorithm, Differential Evolution (DE) is usually criticized for its slow convergence when compared to Particle Swarm Optimization (PSO) on the PSO's benchmark functions. In this paper, by combing the merits of PSO and DE, we first present a new hybrid DE algorithm to accelerate its convergence speed. Then a novel mutation strategy with local and global search operators is proposed for balancing the exploration ability and the convergence rate of the improved DE. The new algorithm is applied to a set of benchmark test problems and compared with basic PSO and DE algorithms and their variants. The experimental results show the new algorithm shows better achievements on most test problems.

[1]  Yuelin Gao,et al.  A Particle Swarm Optimization Algorithm with Logarithm Decreasing Inertia Weight and Chaos Mutation , 2008, 2008 International Conference on Computational Intelligence and Security.

[2]  Frederico G. Guimarães,et al.  A Differential Mutation operator for the archive population of multi-objective evolutionary algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[3]  Arthur C. Sanderson,et al.  Pareto-based multi-objective differential evolution , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[4]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[5]  Renato A. Krohling,et al.  Gaussian particle swarm with jumps , 2005, 2005 IEEE Congress on Evolutionary Computation.

[6]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[7]  Ponnuthurai N. Suganthan,et al.  An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Leandro dos Santos Coelho,et al.  An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications , 2009, Reliab. Eng. Syst. Saf..

[9]  Hak-Keung Lam,et al.  Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[11]  Ruhul A. Sarker,et al.  An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems , 2013, IEEE Transactions on Industrial Informatics.

[12]  Donald Davendra,et al.  Differential Evolution for Permutation—Based Combinatorial Problems , 2009 .

[13]  Carlos A. Coello Coello,et al.  A comparative study of differential evolution variants for global optimization , 2006, GECCO.

[14]  I. Daubechies.  Ten Lectures on Wavelets , 1992 .

[15]  Hui Li,et al.  Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[17]  Uday K. Chakraborty,et al.  Advances in Differential Evolution , 2010 .

[18]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[19]  Oriane M. Neto,et al.  Using differential evolution for combinatorial optimization: A general approach , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[20]  Hao Gao,et al.  Particle swarm algorithm with hybrid mutation strategy , 2011, Appl. Soft Comput..

[21]  Hao Gao,et al.  A New Particle Swarm Algorithm and Its Globally Convergent Modifications , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).