Some Improvements of the Self-Adaptive jDE Algorithm

Abstract

Differential Evolution (DE) is widely used in real- parameter optimization problems in many domains, such as single objective optimization, constrained optimization, multi-modal optimization, and multi-objective optimization. Self-adaptive DE algorithm, called jDE, was introduced in 2006, and since then many other DE-based algorithms were proposed and many excellent mechanisms have improved DE a lot. In this paper we adopt two mutation strategies into the jDE algorithm. Additionally, the new algorithm (jDErpo) uses a gradually increasing mechanism for controlling lower bound of control parameters, JADE's mechanism for a mutant vector if some their components are out of bounds of a search space. Experimental results of the new algorithm are presented using CEC 2013 benchmark functions. The obtained results show that new mechanisms improve performance of the jDE algorithm and the jDErpo algorithm indicates competitive performance compared with the best DE-based algorithms at CEC 2013.