Paper Info
Title
Multiobjective decomposition-based Mallows Models estimation of distribution algorithm. A case of study for permutation flowshop scheduling problem.
Abstract
Mallows Models and Generalized Mallows Models have demonstrated their validity in the context of EDAs to deal with permutation-based optimization problems.We introduce a novel general multi-objective decomposition-based Mallows Models EDA for solving multi-objective permutation optimization problems.We show the potentiality of the proposed framework for solving the multi-objective permutation flowshop scheduling problem minimizing total flow time and makespan. Estimation of distribution algorithms (EDAs) have become a reliable alternative to solve a broad range of single and multi-objective optimization problems. Recently, distance-based exponential models, such as Mallows Model (MM) and Generalized Mallows Model (GMM), have demonstrated their validity in the context of EDAs to deal with permutation-based optimization problems. The aim of this paper is two-fold. First, we introduce a novel general multi-objective decomposition-based EDA using Kernels of Mallows models (MEDA/D-MK framework) for solving multi-objective permutation-based optimization problems. Second, in order to demonstrate the validity of the MEDA/D-MK, we have applied it to solve the multi-objective permutation flowshop scheduling problem (MoPFSP) minimizing the total flow time and the makespan. The permutation flowshop scheduling problem is one of the most studied problems of this kind due to its fields of application and algorithmic challenge. The results of our experiments show that MEDA/D-MK outperforms an improved MOEA/D variant specific tailored for minimizing makespan and total flowtime. Furthermore, our approach achieves competitive results compared to the best-known approximated Pareto fronts reported in the literature for the benchmark considered.
Year
DOI
Venue
2017
10.1016/j.ins.2017.02.034
Inf. Sci.
Keywords
Field
DocType
Estimation of distribution algorithm,Mallows models,Multi-objective optimization,Decomposition-based,Permutation optimization problems,Flowshop scheduling problem
EDAS,Mathematical optimization,Job shop scheduling,Estimation of distribution algorithm,Permutation,Multi-objective optimization,Artificial intelligence,MEDA,Optimization problem,Machine learning,Mathematics,Pareto principle
Journal
Volume
Issue
ISSN
397
C
0020-0255
Citations 
PageRank 
References 
11
0.48
32
Authors
4
Name
Order
Citations
PageRank
Murilo Zangari1172.25
Alexander Mendiburu235533.61
Roberto Santana322027.93
Aurora Trinidad Ramirez Pozo440646.48