Improving Performance of the Differential Evolution Algorithm Using Cyclic Decloning and Changeable Population Size

Differential evolution (DE) is a stochastic global optimization method that has been under continuous development during the past two decades. It has been recognized that preserving the diversification of population can significantly improve the performance of DE. Although, several results and approaches to population diversification have been proposed, it seems that this issue still has a potential for development. In this paper we have studied experimentally the possibility of increasing the performance of DE. Our investigation aims at identifying how the performance of DE depends on such factors as population diversity, size and number of fitness function evaluations carried out by DE to yield a solution. In our experiments we diversified the population in an intensive manner using the proposed decloning procedure carried out in cycles, and also through increasing the population size. The choice of how to preserve the diversification may depend on restrictions imposed on the population size, response time, and the quality of solutions that should be met by a specific implementation of the algorithm. The obtained results allowed us to propose a performance improvement policy that might noteworthy improve both the efficacy and response time of the algorithm. The discrete-continuous scheduling with continuous resource discretisation was used as the test problem.