An Ontology of Preference-Based Multiobjective Metaheuristics

نویسندگان

  • Longmei Li
  • Iryna Yevseyeva
  • Vitor Basto-Fernandes
  • Heike Trautmann
  • Ning Jing
  • Michael Emmerich
چکیده

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.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Nature-inspired metaheuristics for multiobjective activity crashing

Many project tasks and manufacturing processes consist of interdependent timerelated activities that can be represented as networks. Deciding which of these subprocesses should receive extra resources to speed up the whole network (i. e., where activity crashing should be applied) usually involves the pursuit of multiple objectives amid a lack of a priori preference information. A common decisi...

متن کامل

Analyzing the Effect of Objective Correlation on the Efficient Set of MNK-Landscapes

In multiobjective combinatorial optimization, there exists two main classes of metaheuristics, based either on multiple aggregations, or on a dominance relation. As in the single-objective case, the structure of the search space can explain the difficulty for multiobjective metaheuristics, and guide the design of such methods. In this work we analyze the properties of multiobjective combinatori...

متن کامل

Interactive Multiobjective Evolutionary Algorithms

This chapter describes various approaches to the use of evolutionary algorithms and other metaheuristics in interactive multiobjective optimization. We distinguish the traditional approach to interactive analysis with the use of single objective metaheuristics, the semi-a posteriori approach with interactive selection from a set of solutions generated by a multiobjective metaheuristic, and spec...

متن کامل

An Object-Oriented Framework for Multiobjective Local Search

A growing attention has been devoted in recent years to optimisation in multiobjective contexts. As part of this increasing interest, metaheuristics are being adapted to handle multiple objectives, in an effort that has been motivated by the success of metaheuristics in single-objective contexts. Also as a result of significant recent attention, a number of object-oriented approaches for single...

متن کامل

Adding diversity to two multiobjective constructive metaheuristics for time and space assembly line balancing

We present a new mechanism to introduce diversity into two multiobjective approaches based on ant colony optimisation and randomised greedy algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. Promising results are shown after applying the designed constructive metaheuristics to ten real-like problem instances.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2016