A novel binary differential evolution algorithm based on artificial immune system

Differential evolution, a heuristic optimization algorithm, has been successful in solving a wide range of real-value optimization problems. However, it is of low efficiency in dealing with the discrete problems. Tn this paper, a new binary differential evolution algorithm based on the theory of immunity in biology is proposed. In spite of the simplicity of the technique, simulation results show the improvement of the searching ability and increment in the convergence speed in comparison with the other binary particle swarm optimization.

[1]  Joni-Kristian Kämäräinen,et al.  Differential Evolution Training Algorithm for Feed-Forward Neural Networks , 2003, Neural Processing Letters.

[2]  G. J. V. Nossal,et al.  Negative selection of lymphocytes , 1994, Cell.

[3]  Dimitris K. Tasoulis,et al.  Vector evaluated differential evolution for multiobjective optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

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

[5]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[6]  Jonathan Timmis,et al.  Artificial immune systems as a novel soft computing paradigm , 2003, Soft Comput..

[7]  Yongling Zheng,et al.  On the convergence analysis and parameter selection in particle swarm optimization , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).

[8]  Piero P. Bonissone,et al.  Fuzzy Logic Controlled Multi-Objective Differential Evolution , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..