Paper Info
Title
Probabilistic Analysis of Pareto Front Approximation for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm
Abstract
Metaheuristics that explore the decision variables space to construct probabilistic modeling from promising solutions, like estimation of distribution algorithms (EDAs), are becoming very popular in the context of Multi-objective Evolutionary Algorithms (MOEAs). The probabilistic model used in EDAs captures certain statistics of problem variables and their interdependencies. Moreover, the incorporation of local search methods tends to achieve synergy of MOEAs' operators and local heuristics aiming to improve the performance. In this work, we aim to scrutinize the probabilistic graphic model (PGM) presented in Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), which is based on a Bayesian network. Different from traditional EDA-based approaches, the PGM of HMOBEDA provides the joint probability of decision variables, objectives, and configuration parameters of an embedded local search. HMOBEDA has shown to be very competitive on instances of Multi-Objective Knapsack Problem (MOKP), outperforming state-of-the-art approaches. Two variants of HMOBEDA are proposed in this paper using different sample methods. We aim to compare the learnt structure in terms of the probabilistic Pareto Front approximation produced at the end of evolution. Results on instances of MOKP with 2 to 8 objectives show that both proposed variants outperformthe original approach, providing not only the best values for hypervolume and inverted generational distance indicators, butalso a higher diversity in the solution set.
Year
DOI
Venue
2017
10.1109/BRACIS.2017.32
2017 Brazilian Conference on Intelligent Systems (BRACIS)
Keywords
Field
DocType
Multi-objective optimization,estimation of distribution algorithms,automatic algorithm configuration
Mathematical optimization,Joint probability distribution,Evolutionary algorithm,Estimation of distribution algorithm,Computer science,Probabilistic analysis of algorithms,Multi-objective optimization,Bayesian network,Local search (optimization),Probabilistic logic
Conference
ISBN
Citations 
PageRank 
978-1-5386-2408-1
1
0.35
References 
Authors
14
6
Name
Order
Citations
PageRank
Marcella Scoczynski Ribeiro Martins110.35
Myriam Regattieri Delgado222422.26
Ricardo Lüders3247.23
Roberto Santana422027.93
Richard Gonçalves5235.79
Carolina P. de Almeida6204.39