Differential Evolution strategy based on the constraint of fitness values classification

This paper presents a new Differential Evolution (DE) strategy, named as FCDE, based on the constraint of classification of fitness function values. To ensure the population could move to the better fitness landscape, the global fitness value distribution information of the objective function are used and all points in the population are classified into three class by their fitness values in each generation, so the points in each class choose their donor vector and differential vector from the points in adjacent senior class to form the trial vector. This strategy could speed up the convergence to global optimal as well as avoid falling into the local optimal. Another attractive character of FCDE is the control parameters in this DE variant are self-adaptive. This method is tested on the 30 benchmark functions of CEC2014 special session and competition on single objective real-parameter numerical optimization. The experimental results showed acceptable reliability of this strategy in high search dimension. This paper will participate in the competition on real parameter single objective optimization to compare with other algorithms.