Multiobjective real-coded bayesian optimization algorithmrevisited: diversity preservation

Ahn, Chang Wook; Ramakrishna, R. S.

doi:10.1145/1276958.1277079

Cited by 14 publications

(24 citation statements)

References 10 publications

(37 reference statements)

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“…This is a point that has already been made, and some proposals for addressing the issue have been laid out [23,24,25,26,27]. This loss of diversity can be traced back to the above outliers issue of model-building algorithms.…”

Section: The Model-building Issuementioning

confidence: 99%

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Martí

García

Berlanga

et al. 2011

Lecture Notes in Computer Science

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Abstract. The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have a intrinsic shortcoming in their model-building algorithms that hamper their performance.In this work we argue that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.

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Section: The Model-building Issuementioning

confidence: 99%

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Martí

García

Berlanga

et al. 2011

Lecture Notes in Computer Science

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“…The variables are modeled as feature nodes and objectives as continuous-valued class nodes. The feature subgraph encodes the dependencies between problem variables like the models learnt by other EDAs that use Bayesian networks as their probabilistic model [34]- [39]. The bridge and class subgraphs, however, encode new types of dependencies.…”

Section: B An Eda Based On Mbn Estimationmentioning

confidence: 99%

Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

Karshenas

Santana

Bielza

et al. 2014

IEEE Trans. Evol. Computat.

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Abstract-This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorithm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.

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“…The feature subgraph of MBN encodes the relationships between variables like the models learnt by other Bayesian network-based multi-objective EDAs [43][44][45][46][47][48]. However, the bridge and class subgraphs, encode new types of relationships as the result of joint modeling of variables and objectives.…”

Section: Multi-objective Optimization With Joint Variable-objective Pmentioning

confidence: 99%

Interval-based ranking in noisy evolutionary multi-objective optimization

2014

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As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present α-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standard multi-objective evolution ary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experi mental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization.

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Multiobjective real-coded bayesian optimization algorithmrevisited: diversity preservation

Cited by 14 publications

References 10 publications

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

Interval-based ranking in noisy evolutionary multi-objective optimization

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