A multi-objective optimization algorithm based on gradient information

被引：0

作者：

Qi, Rongbin ^{[1
,2
]}

Liu, Chenxia ^{[1
,2
]}

Zhong, Weimin ^{[1
,2
]}

Qian, Feng ^{[1
,2
]}

机构：

[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education

[2] School of Information Science and Engineering, East China University of Science and Technology

来源：

Huagong Xuebao/CIESC Journal | 2013年 / 64卷 / 12期

关键词：

Dynamic optimization; Fed-batch bioreactor; Gradient information; Multi-objective; Optimization algorithm; Selection and collocation method;

D O I：

10.3969/j.issn.0438-1157.2013.12.020

中图分类号：

学科分类号：

摘要：

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. © All Rights Reserved.

引用

页码：4401 / 4409

页数：8

共 20 条

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