RDE - Reconstructed mutation strategy for differential evolution algorithm

Abstract

Several researchers’ innovative work during past years has led to development of numerous optimization techniques. Complex task that were once difficult to be compute using traditional methods now can use the optimization techniques for computation. Differential Evolution (DE) is a powerful, population based, stochastic optimization algorithm. The mutation strategy of DE algorithm is an important operator as it aids in generating a new solution vector. In this paper, we are introducing a variant of DE mutation strategy named RDE (Reconstructed Differential Evolution). This strategy use three different control parameters. The results computed here are then compared with the results of an existing mutation strategy where in the comparison show a better performance for the new revised strategy.