An elite-guided hierarchical differential evolution algorithm

Population structure has an impact on the performance of metaheuristic algorithms. To better improve the performance of differential evolution (DE), an elite-guided hierarchical differential evolution algorithm (EHDE) is proposed. First, an elite-guided hierarchical mutation mechanism is presented, which integrates elite elements into the hierarchical population structure. During each generation, the population is divided into three groups according to fitness values, each group playing a unique role in its hierarchy. The best individual on the top layer is used to avoid the local optimal by random reinitialization or Lévy flight. The ( k − 1) elite individuals on the middle layer focus on the local search around the best individual. The remaining non-elite individuals on the bottom layer pay more attention to a more considerable range search by the guidance of the k elite individuals. Second, to accommodate diverse optimization problems and seek the balance between exploration and exploitation, the adaptive strategy of EHDE control parameters has added the random component and the time-varying component. Finally, for the sake of evaluating the performance of EHDE, sensitivity analysis to the size of elite individuals, efficiency analysis of the control parameters adaptive strategy, and comparisons with nine advanced DE variants and three non-DE algorithms on 29 universal benchmark function in terms of convergence accuracy and convergence speed have been taken out. All the obtained results show that the proposed EHDE has excellent optimization performance.