Most of the basic multi-objective evolutionary algorithm is one kind of similar random search algorithm based on the concept of Pareto optimization

which with the slowly speed

especially for dynamic multi-objective problems.Accordingly

the hybrid optimization algorithm based on single and multi-objective gradient information (HSMGOA) is proposed.The algorithm confirms the direction of variation on each individual by using the gradient information.Firstly

the negative gradient direction information of each target in the population is calculated

those operation guarantees the individual species moving to the optimization declining direction for each of the single objective value effectively.Due to the conflict between each objective of multi-objective problem

it may cause the increase of the other objective function value if only considering the fall direction of one target.Therefore

this article also joins in the random weighted integration method

which fusing the gradient direction information of multiple goals to one search direction.Also

based on the traditional crowding distance selection method

this paper proposes a new select scatter point method to further speed up the optimization algorithm and provide the best initial population.Through the simulation of ZDT series test function and the analysis results with the NSGA2

it can be seen that the performance of the proposed algorithm is much better than the NSGA2 algorithm with less run times.It also shows that this algorithm has faster convergence.Finally a new algorithm was proposed by mixing the algorithm with NSGA2

and it is applied to dynamic multi-objective optimization of fed-batch bioreactor

the preferable Pareto optimal solution set is obtained.Compared with NSGA2 and MOPSO

the new algorithm shows better performance.