A differential evolution algorithm with success-based parameter adaptation for CEC2015 learning-based optimization

Developing efficient evolutionary algorithms for solving learning-based real-parameter single objective optimization is a very challenging and essential task in many real applications. This task involves finding the best optimal solution with least computational cost, avoiding premature convergence. This paper proposes a new efficient Differential Evolution algorithm with success-based parameter adaptation with resizing population space. We introduce a new technique to adapt the control parameters which uses a memory-based structure of previous successful settings. Moreover, the population size is adapted linearly to find the most suitable size which helps to guide the search in each optimization loop. The proposed algorithm is tested on the benchmarks of the CEC2015 real parameter single objective competition. The results affirm the efficiency and robustness of our approach to reach good results.