A hybrid equilibrium optimizer with differential evolution algorithm
Meijun Duan, Chao Zhang, Bo Li · 2023
For the population diversity decreasing rapidly in differential evolution (DE), a novel hybrid algorithm based on Equilibrium Optimizer (EO) and DE (HEODE) is presented. Firstly, a sharing learning mutation strategy is introduced in DE based on the sharing individual. By learning from the sharing individual, information exchange among the whole population can be achieved. Secondly, an effective hybridization mechanism is proposed to combine the advantages of two algorithms. A serial of experiments are used for effectiveness verification among 12 DEs and 5 none-DE based algorithms. The test results validate that HEODE outperforms all competitors.