Interactive evolutionary multiobjective optimization for hydraulic valve controller parameters

Gong

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

2011

Multi-objective optimization (MOO) problems with interval parameters are popular and important in real-world applications. Previous evolutionary optimization methods aim to find a set of well-converged and evenly-distributed Pareto-optimal solutions. We present a novel evolutionary algorithm (EA) that interacts with a decision maker (DM) during the optimization process to obtain the DMÊs most preferred solution. First, the theory of a preference polyhedron for an optimization problem with interval parameters is built up. Then, an interactive evolutionary algorithm (IEA) for MOO problems with interval parameters based on the above preference polyhedron is developed. The algorithm periodically provides a part of non-dominated solutions to the DM, and a preference polyhedron, based on which optimal solutions are ranked, is constructed with the worst solution chosen by the DM as the vertex. Finally, our method is tested on two biobjective optimization problems with interval parameters using two different value function types to emulate the DMÊs responses. The experimental results show its simplicity and superiority to the posteriori method.

Section: Related Workmentioning

confidence: 99%

“…Related methods can be grouped into the following three categories, i.e., machine learning based [1,12], fit based [6,19] and preference convex cone/polyhedron cone based [9,18] methods. For the first two methods, pairwise comparisons of all alternatives are conducted.…”

Section: Introductionmentioning

confidence: 99%

Solving interval multi-objective optimization problems using evolutionary algorithms with preference polyhedron

Gong

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

2011

“…Some researchers use machine learning approaches to learn the value function, such as support vector machine (SVM) [101], Artificial Neural Networks (ANN) [102][103][104], instance-based supervised online learning [105].…”

Section: Solution Comparison-based Approachesmentioning

confidence: 99%

An Ontology of Preference-Based Multiobjective Metaheuristics

Li,

Yevseyeva,

Basto-Fernandes

et al. 2016

Preprint

User preference integration is of great importance in multi-objective optimization, in particular in many objective optimization. Preferences have long been considered in traditional multicriteria decision making (MCDM) which is based on mathematical programming. Recently, it is integrated in multi-objective metaheuristics (MOMH), resulting in focus on preferred parts of the Pareto front instead of the whole Pareto front. The number of publications on preference-based multiobjective metaheuristics has increased rapidly over the past decades. There already exist various preference handling methods and MOMH methods, which have been combined in diverse ways. This article proposes to use the Web Ontology Language (OWL) to model and systematize the results developed in this field. A review of the existing work is provided, based on which an ontology is built and instantiated with state-of-the-art results. The OWL ontology is made public and open to future extension. Moreover, the usage of the ontology is exemplified for different usecases, including querying for methods that match an engineering application, bibliometric analysis, checking existence of combinations of preference models and MOMH techniques, and discovering opportunities for new research and open research questions.

At - Automatisierungstechnik

“…So verzichtet die Lösung auf das Totzeit-Übertragungselement auf Kosten einer erhöhten Parameteranzahl für G 2 . In Summe nutzt diese Beispiellösung 16 Parameter anstelle der 29 Parameter des Referenzmodells.8.2 Systemidentifikation eines HydraulikventilsIdentifiziert wird das Systemverhalten eines hydraulischen Wegeventils[23], das verwendet wird, um Richtung und Stärke eines Volumenstroms zu regeln. Dazu wird ein Kolben so positioniert, dass er die entsprechenden Kanäle im Ventil öffnet oder schließt.…”

unclassified

Struktur- und Parameteridentifikation mit Evolutionären Algorithmen

Braun¹,

Krettek

Hoffmann

et al. 2011

Self Cite

Zusammenfassung Modellierung und Identifikation dynamischer Systeme sind wichtige Aspekte der Regelungstechnik. Dieser Beitrag stellt einen Ansatz zur Optimierung von strukturvariablen Modellen dynamischer Systeme mit multikriteriellen evolutionären Algorithmen vor. Die Methode verwendet spezifische evolutionäre Operatoren zur simultanen Optimierung der Struktur eines Modells und ihrer Parameter. Basierend auf Ein-und Ausgangsdaten des realen Systems ermittelt die Strategie eine Menge von optimalen Modellen, die einen Kompromiss zwischen Qualität und Komplexität der Modellierung darstellen. Exemplarisch angewandt wird das Verfahren für die Identifikation eines künstlichen Testsystems sowie eines hydraulischen Proportionalventils und eines elastischen Roboterarms.Summary Modeling and identification of dynamic systems are crucial in the fields of control engineering. This paper presents an approach for optimization of dynamic system models with variable structure using multi-objective evolutionary algorithms. The algorithm uses specific operators to simultaneously evolve the structure of a model and optimize its parameters. Based on input and output data of the real system the method aims at finding a set of optimal models that constitute a compromise between model quality and complexity. The method is exemplary applied to the identification of an artificial test system as well as of a hydraulic proportional valve and an elastic robot arm.