Evaluation of Two-Stage Ensemble Evolutionary Algorithm for Numerical Optimization

In many challenging numerical optimization problems, the conflict between exploitation and exploration abilities of EAs must be balanced in an effective and efficient way. In the previous research, in order to address this issue, the Two-Stage ensemble Evolutionary Algorithm(TSEA)was originally proposed for engineering application. In TSEA, the optimization is divided into two relatively separate stages, which aims at handling the exploitation and exploration in a more reasonable way. In this paper, we try to extend the application area of TSEA from specific engineering problems to general numerical optimization problems by altering its sub-optimizers. The experimental studies presented in this paper contain three aspects: (1) The benefits of the TSEA framework are experimentally investigated by comparing TSEA with its sub-optimizers on 26 test functions; then (2) TSEA is compared with diverse state-of-the-art evolutionary algorithms (EAs) to comprehensively show its advantages; (3) To benchmark the performance of TSEA further, we compare it with 4 classical memetic algorithms (MAs) on CEC05 test functions. The experimental results definitely demonstrate the excellent effectiveness, efficiency and reliability of TSEA.