Exploring the probabilistic graphic model of a hybrid multi-objective Bayesian estimation of distribution algorithm

The Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA) has shown to be very competitive for Many Objective Optimization Problems (MaOPs). The Probabilistic Graphic Model (PGM) of HMOBEDA expands the possibilities for exploration as it provides the joint probability of dec...

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Vydáno v:	Applied soft computing Ročník 73; s. 328 - 343
Hlavní autoři:	Martins, Marcella S.R., Delgado, Myriam, Lüders, Ricardo, Santana, Roberto, Gonçalves, Richard A., de Almeida, Carolina P.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.12.2018
Témata:	Automatic algorithm configuration Estimation of distribution algorithms Multi-objective optimization Estimation of distribution algorithms Automatic algorithm configuration Multi-objective optimization
ISSN:	1568-4946, 1872-9681
On-line přístup:	Získat plný text
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Popis
Shrnutí:	The Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA) has shown to be very competitive for Many Objective Optimization Problems (MaOPs). The Probabilistic Graphic Model (PGM) of HMOBEDA expands the possibilities for exploration as it provides the joint probability of decision variables, objectives, and configuration parameters of an embedded local search. This work investigates different sampling mechanisms of HMOBEDA, applying the considered approaches to two different combinatorial MaOPs. Moreover, the paper provides a broad set of statistical analyses on its PGM model. These analyses have been carried out to evaluate how the interactions among variables, objectives and local search parameters are captured by the model and how information collected from different runs can be aggregated and explored in a Probabilistic Pareto Front. In experiments, two variants of HMOBEDA are compared with the original version, each one with a different set of evidences fixed during the sampling process. Results for instances of multi-objective knapsack problem with 2–5 and 8 objectives show that the best variant outperforms the original HMOBEDA in terms of convergence and diversity in the solution set. This best variant is then compared with five state-of-the-art evolutionary algorithms using the knapsack problem instances as well as a set of MNK-landscape instances with 2, 3, 5 and 8 objectives. HMOBEDA outperforms all of them. [Display omitted] •An approach for multi and many-objective combinatorial optimization is explored.•It is based on a joint probabilistic model with local optimizers as an online tuning.•Versions with different sampling are analyzed from a probabilistic point of view.•The best version outperforms other approaches when the number of objectives increases.•Information can be extracted from the models to learn and explore dependencies.
ISSN:	1568-4946 1872-9681
DOI:	10.1016/j.asoc.2018.08.039