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

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 sequ...

Full description

Saved in:
Bibliographic Details
Published in:Process integration and optimization for sustainability 2022-03, Vol.6 (1), p.161-174
Main Authors: Hernández-Pérez, Luis Germán, Ponce-Ortega, José María
Format: Article
Language:English
Subjects:
Algorithms
Case studies
Economics and Management
Energy Policy
Engineering
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Heuristic methods
Industrial and Production Engineering
Industrial Chemistry/Chemical Engineering
Integer programming
Linear programming
Mathematical models
Mathematical programming
Methods
Multiple objective analysis
Optimization
Optimization algorithms
Optimization techniques
Original Research Paper
Parameter uncertainty
Performance evaluation
Process planning
Software
Stochasticity
Sustainable Development
Visual Basic for Applications
Waste Management/Waste Technology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2509-4238
2509-4246
DOI:10.1007/s41660-021-00211-5