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.
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收藏
页码:4401 / 4409
页数:8
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共 20 条
  • [1] Deb K., Pratap A., Agarwal S., Meyarivan T., A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6, 2, pp. 182-197, (2002)
  • [2] Laumanns M., Thiele L., Deb K., Zitzler E., Combining convergence and diversity in evolutionary multi-objective optimization, Evolutionary Computation, 10, 3, pp. 263-282, (2002)
  • [3] Brockhoff D., Zitzler E., Are all objectives necessary on dimensionality reduction in evolutionary multi-objective optimization, Parallel Problem Solving from Nature-PPSN IX, pp. 533-542, (2006)
  • [4] Hernandez-Diaz A.G., Santana-Quintero L.V., Coello C.A., Molina J., Pareto-adaptive-dominance, Evolutionary Computation, 15, 4, pp. 533-542, (2007)
  • [5] Deb K., Saxena D.K., On finding Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems, (2005)
  • [6] Lahanas M., Baltas D., Giannouli S., Global convergence analysis of fast multi-objective gradient based dose optimization algorithms for high-dose-rate brachytherapy, Phys. Med. Biol., 48, 5, pp. 599-617, (2003)
  • [7] Salomon R., Evolutionary algorithm and gradient search: similarities and differences, IEEE Transactions on Evolutionary Computation, 2, 2, pp. 45-55, (1998)
  • [8] Goh C.K., Ong Y.S., Tan K.C., Teoh E.J., An investigation on evolutionary gradient search for multi-objective optimization, Evolutionary Computation, pp. 3741-3746, (2008)
  • [9] Smaili A.A., Diab N.A., Atallah N.A., Optimum synthesis of mechanisms using tabu-gradient search algorithm, Journal of Mechanical Design, 127, 5, pp. 917-923, (2004)
  • [10] Shukla P.K., On gradient based local search methods in unconstrained evolutionary multi-objective optimization, Evolutionary Multi-Criterion Optimization, pp. 96-110, (2007)