Use of Statistic Functions to Consider Uncertainty in Multi-objective Optimization Methods Based on Metaheuristic Algorithms

被引：0
作者：
Luis Germán Hernández-Pérez
José María Ponce-Ortega
机构：
[1] Universidad Michoacana de San Nicolás de Hidalgo,Chemical Engineering Department
来源：
Process Integration and Optimization for Sustainability | 2022年 / 6卷
关键词：
Multi-objective optimization; Evolutionary algorithms; Metaheuristic tools; Optimization under uncertainty; Stochastic optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper presents a new optimization procedure to consider uncertainty in complex optimization problems, where conventional mathematical programming strategies are not efficient. This methodology consists in using evolutionary algorithms based on metaheuristic optimization tools. The proposed sequence involves the generation of random values to consider the stochastic behavior of uncertain parameters in the mathematical model. Likewise, a subcode was developed in visual basic for applications to manipulate the decision variables generated by the evolutionary algorithm and the post evaluation of the performance of the objective functions. The used metaheuristic optimization algorithm was the improved multi-objective differential evolution optimization algorithm. To prove the efficiency of the proposed optimization strategy, three case studies are analyzed, which involve the solution of single-objective and bi-objective optimization problems. This optimization procedure offers attractive solutions to the specified goals.
引用
收藏
页码：161 / 174
页数：13
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