A Multi-objective Fuzzy Optimization Method of Resource Input Based on Genetic Algorithm

Authors: Tao Zhao, Xin Wang

Abstract:

With the increasing complexity of engineering problems, the traditional, single-objective and deterministic optimization method can not meet people-s requirements. A multi-objective fuzzy optimization model of resource input is built for M chlor-alkali chemical eco-industrial park in this paper. First, the model is changed into the form that can be solved by genetic algorithm using fuzzy theory. And then, a fitness function is constructed for genetic algorithm. Finally, a numerical example is presented to show that the method compared with traditional single-objective optimization method is more practical and efficient.

Keywords: Fitness function, genetic algorithm, multi-objectivefuzzy optimization, satisfaction degree membership function.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332368

References:

[1] Xiaoli Zhang, Jianqiang Yang, Chunying Chang, Wei Dong, "Multi-objective fuzzy optimization method and its practical application to engineering design," Journal of Dalian University of Technology, vol. 45, pp. 374-378, 2005.
[2] Yong Liao, "Study on evolutionary algorithm for fuzzy multi-objective optimization problems," Wuhan University, 2003.
[3] Toshihiko Nakata, "Energy-economic models and the environment," Progress in Energy and Combustion Science, vol. 30, pp. 417-475, 2004.
[4] Baoding Liu, Introduction to Uncertain Programming, Beijing: Tsinghua University, 2005.
[5] Hun Kuk, Tetsuzo Tanino and Masahiro Tanaka, "Sensitivity Analysis in Parametrized Convex Vector Optimization," Journal of Mathematical Analysis and Applications, vol. 202, pp. 501-524, 1996.