Focusing on the demerits of AEA (Alopex-based evolutionary algorithm)

this paper proposed a modified AEA algorithm (MAEA)

which was fused AEA with differential evolution (DE). In order to enhance the performance of the algorithm

the generation method of population in AEA was improved by applying an improved DE operation to AEA. The modified algorithm not only takes advantage of heuristic search and deterministic search of AEA

but also increases the population diversity and is adapted to global search and local search. Then the MAEA algorithm was tested by 21 benchmark functions

the results show that MAEA outperforms DE

MDE and AEA. Further comparison results between MAEA algorithm and a representative state-of-the-art algorithm(ISDEMS) indicate that

the performance of the modified algorithm is significantly improved

in both accuracy and stability. Furthermore

the algorithm was applied to the parameter estimation of the models of fermentation dynamics

and the satisfactory results were obtained.