A Hybrid Ant Colony Differential Evolution and its application to water resources problems

Differential Evolution (DE) is generally considered as a reliable, accurate and robust optimization technique. However, the algorithm suffers from slow convergence rate and takes large computational time for optimizing the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This research introduces a modified differential evolution, called Ant Colony Differential Evolution, ACDE. The ACDE algorithm initializes the population using opposition based learning, in mutation phase it applies random localization technique and it simulates the movement of ants to refine the best solution found in each generation. Also, it maintains a single set of population while updating the population for next generation. ACDE validated on a test bed of 7 benchmark problems and two real life problems and the numerical results are compared with original DE. It is found that ACDE requires less computational effort to locate global optimal solution without compromising with the quality of solution.

[1]  E. O'Loughlin,et al.  DILUTION AND DECAY OF AQUATIC HERBICIDES IN FLOWING CHANNELS , 1975 .

[2]  Andries Petrus Engelbrecht,et al.  Differential evolution methods for unsupervised image classification , 2005, 2005 IEEE Congress on Evolutionary Computation.

[3]  N. Sahinidis,et al.  Global optimization of nonconvex NLPs and MINLPs with applications in process design , 1995 .

[4]  Xiaodong Wang,et al.  Particle Swarm Optimization for Calibrating Stream Water Quality Model , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[5]  Patrick Siarry,et al.  Particle swarm and ant colony algorithms hybridized for improved continuous optimization , 2007, Appl. Math. Comput..

[6]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[7]  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).

[8]  Rainer Storn,et al.  Differential Evolution Design of an IIR-Filter with Requirements for Magnitude and Group Delay , 1995 .

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

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

[11]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[12]  Sandra Paterlini,et al.  High performance clustering with differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[13]  Linus Schrage,et al.  Modeling and Optimization With Gino , 1986 .