An improved JADE algorithm for global optimization
Ming Yang, Zhihua Cai, Changhe Li, Jing Guan · 2014
In differential evolution (DE), the optimal value of the control parameters is problem-dependent. Many improved DE algorithms have been proposed with the aim of improving the effectiveness for solving general problems. As a very known adaptive DE algorithm, JADE adjusts the crossover probability CR of each individual by a norm distribution, in which the value of standard deviation is fixed, based on its historical record of success. The fixed and small standard deviation results in that the generated CR may not suitable for solving a problem. This paper proposed an improvement for the adaptation of CR, in which the standard deviation is adaptive. The diversity of values of CR was improved. This improvement was incorporated into the JADE algorithm and tested on a set of 25 scalable benchmark functions. The results showed that the adaptation of CR improved the performance of the JADE algorithm, particularly in comparisons with several other peer algorithms on high-dimensional functions.