Differential evolution with improved elite archive mutation and dynamic parameter adjustment

Control parameters and mutation methods impact upon the global search ability of differential evolution algorithm (DE), and varying optimization issues own varying parameter settings. In this paper, an enhanced elite archive mutation strategy with self-adaption parameter adjustment (EAMSADE) is proposed to raise DE’s performance. The population’s diversity and the individual’s difference are considered by this paper to enhance the algorithm’s convergence property. EAMSADE amends the DE/rand/1 strategy by means of enhanced elite archive mutation and modifies parameters (crossover rate and scaling factor) adaptively which is based on quantitative analysis of individual variability and population diversity. To confirm the proposed EAMSADE’s performance, a suit of 21 benchmark functions from IEEE CEC2005 are utilized to carry out the experiment. The outcome of the experiment confirms that the proposed EAMSADE has got an overall improvement on convergence performance and global search ability compared to the other four amended DE.