[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

Journal of Information Science and Engineering, Vol. 38 No. 3, pp. 645-663

L∞ Metric based Multi-Objective Differential Evolution Algorithm and its Industrial Application

1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education
East China University of Science and Technology
Shanghai, 200237 P.R. China

2School of Electrical and Computer Engineering
Purdue University
West Lafayette, IN 47907, USA

3Shanghai Institute of Intelligent Science and Technology
Tongji University
Shanghai, 200237 P.R. China
E-mail: xfyan@ecust.edu.cn

In multi-objective optimization problems, the objective space of fitness functions has a close relationship with the solution space. Extracting the optimal direction and optimal parameter information are very useful for the optimization process. This paper proposes multi-objective differential evolution algorithm with a clustering based objective space division and parameter adaptation (MODECD). L metric matrix based optimal strategy is used to split the objective space into sub-spaces and to extract the optimal directions. A fitness value based parameter adaptation and mutation strategy are used to extract the op-timal strategy information. The results with 20 benchmark tests show the competitiveness of the MODECD algorithm in both convergence speed and diversity of solution approxi-mating the Pareto front. In addition, MODECD is used to optimize the fermentation pro-cess of sodium gluconate as an example of its superior performance in solving real-world problems.

Keywords: differential evolution algorithm, L∞ metric, parameter adaptation, multi-objective optimization, sodium gluconate production

  Retrieve PDF document (JISE_202203_09.pdf)