A Hybrid Differential Evolution Method for Practical Engineering Problems

This paper introduced an almost control parameters free modified differential evolution for global optimization problems. The modifications are derived from the mechanisms of particle swarm optimization viz., topologies, inertial weight, neighborhood best and individual best, with which each individual performed the mutation operator based on its current position, the neighborhood best and its individual best along with the inertial weight. And the crossover operator in traditional differential evolution is removed, while the selection operator was employed. The approach was employed for a tension/compression string design problem and an economic dispatch problem in power system. By comparisons with the other evolutionary algorithms, the proposed approach has shown its feasibility and effectiveness.

[1]  Ashok Dhondu Belegundu,et al.  A Study of Mathematical Programming Methods for Structural Optimization , 1985 .

[2]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[3]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

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

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

[6]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[7]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.

[8]  Liu Fang APPLICATION OF IMPROVED PARTICLE SWARM OPTIMIZATION IN ECONOMIC DISPATCHING , 2005 .

[9]  Jin-Kyo Chong,et al.  Differential Evolution Strategy for Constrained Global Optimization and Application to Practical Engineering Problems , 2006, 2006 12th Biennial IEEE Conference on Electromagnetic Field Computation.

[10]  D. Lowther,et al.  Differential Evolution Strategy for Constrained Global Optimization and Application to Practical Engineering Problems , 2006, IEEE Transactions on Magnetics.