Real-World Applications of Multiobjective Optimization

Stewart, Theodor J.; Bandte, Oliver; Braun, Heinrich; Chakraborti, Nirupam; Ehrgott, Matthias; Göbelt, Mathias; Jin, Yaochu; Nakayama, Hirotaka; Poles, Silvia; Stefano, Danilo Di

doi:10.1007/978-3-540-88908-3_11

Cited by 41 publications

(41 citation statements)

References 47 publications

(42 reference statements)

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“…This type of problems, although counterintuitive and hard to visualize for a human decision maker, are not uncommon in real-life engineering practice (c. f. [122]). …”

Section: Introductionmentioning

confidence: 98%

MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

et al. 2016

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The extension of estimation of distribution algorithms (EDAs) to the multiobjective domain has led to multi-objective optimization EDAs (MOEDAs). Most MOEDAs have limited themselves to porting single-objective EDAs to the multi-objective domain. Although MOEDAs have proved to be a valid approach, the last point is an obstacle to the achievement of a significant improvement regarding "standard" multi-objective optimization evolutionary algorithms. Adapting the model-building algorithm is one way to achieve a substantial advance. Most model-building schemes used so far by EDAs employ off-the-shelf machine learning methods. However, the model-building problem has particular requirements that those methods do not meet and even evade. The focus of this paper is on the modelbuilding issue and how it has not been properly understood and addressed by most MOEDAs. We delve down into the roots of this matter and hypothesize about its causes. To gain a deeper understanding of the subject we propose a novel algorithm intended to overcome the drawbacks of current MOEDAs. This new algorithm is the multi-objective neural estimation of distribution algorithm (MONEDA). MONEDA uses a modified growing neural gas network for model-building (MB-GNG). MB-GNG is a custom-made clustering algorithm that meets the above demands. Thanks to its custom-made model-building algorithm, the preservation of elite individuals and its individual replacement scheme, MONEDA is capable of scalably B Luis Martí 123 J Glob Optim solving continuous multi-objective optimization problems. It performs better than similar algorithms in terms of a set of quality indicators and computational resource requirements.

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“…This type of problems, although counterintuitive and hard to visualize for a human decision maker, are not uncommon in real-life engineering practice (c. f. [122]). …”

Section: Introductionmentioning

confidence: 98%

MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Although somewhat counterintuitive and hard to visualize for a human decision maker, these problems are not uncommon in real-life engineering practice. For example, [48] details some relevant real problems of this type.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

Martí

García

Berlanga

et al. 2012

Ann Math Artif Intell

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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 an intrinsic shortcoming in their modelbuilding algorithms that hamper their performance. In this work, we put forward the argument 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 a hypervolumebased 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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“…Although somewhat counterintuitive and hard to visualize for a human decision maker, these problems are not uncommon in real-life engineering practice. For example, [5] details some relevant real problems of this type.…”

Section: Introductionmentioning

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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Real-World Applications of Multiobjective Optimization

Cited by 41 publications

References 47 publications

MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm

Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

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

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