Improving the computational efficiency in a global formulation (GLIDE) for interactive multiobjective optimization

@article{Ruiz2012ImprovingTC,
  title={Improving the computational efficiency in a global formulation (GLIDE) for interactive multiobjective optimization},
  author={Francisco Ruiz and Mariano Luque and Kaisa Miettinen},
  journal={Annals of Operations Research},
  year={2012},
  volume={197},
  pages={47-70}
}
In this paper, we present a new general formulation for multiobjective optimization that can accommodate several interactive methods of different types (regarding various types of preference information required from the decision maker). This formulation provides a comfortable implementation framework for a general interactive system and allows the decision maker to conveniently apply several interactive methods in one solution process. In other words, the decision maker can at each iteration… 
View on Springer
kth.diva-portal.org
26 Citations
Highly Influential Citations
1
Background Citations
6
Methods Citations
6

26 Citations

Comparing interactive evolutionary multiobjective optimization methods with an artificial decision maker

This paper proposes ADM-II, which is tailored to assess several interactive evolutionary methods and is able to handle different types of preference information, and considers two phases of interactive solution processes separately, so that the proposed ADm-II generates preference information in different ways in each of them to reflect the nature of the phases.

Interactive Multiobjective Optimization: A Review of the State-of-the-Art

A review of IMO research from both MCDM and EMO perspectives is given, taking into account four classification criteria including the interaction pattern, preference information, preference model, and search engine.

Towards explainable interactive multiobjective optimization: R-XIMO

This work introduces a new method, R-XIMO, which provides explanations to a decision maker for reference point based interactive methods and utilizes concepts of explainable artificial intelligence and SHAP (Shapley Additive exPlanations) values.

Designing empirical experiments to compare interactive multiobjective optimization methods

A novel questionnaire and experimental design is proposed and, as proof of concept, they are applied in comparing two methods, and a sustainability problem with multiple objectives is introduced.

A new preference handling technique for interactive multiobjective optimization without trading-off

A technique for handling decision maker’s preferences is proposed, which eliminates the necessity to think in terms of trade-offs and is incorporated into an interactive trade-off-free method for multiobjective optimization.

Interactive Nonlinear Multiobjective Optimization Methods

An overview of interactive methods for solving nonlinear multiobjective optimization problems is given and methods presented are classified according to the type of preference information that the decision maker is assumed to provide.

E-NAUTILUS: A decision support system for complex multiobjective optimization problems based on the NAUTILUS method

Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms

The proposed interactive RVEA is applied to the multiple-disk clutch brake design problem with five objectives to demonstrate the potential of the idea in supporting decision making in optimization problems involving more than three objectives.

A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

This paper surveys surrogate-based methods proposed in the literature, where the methods are independent of the underlying optimization algorithm and mitigate the computational burden to capture different types of Pareto frontiers.

A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

This paper surveys surrogate-based methods proposed in the literature, where the methods are independent of the underlying optimization algorithm and mitigate the computational burden to capture different types of Pareto frontiers.

References

SHOWING 1-10 OF 68 REFERENCES

Global formulation for interactive multiobjective optimization

This work introduces a general formulation that can accommodate several interactive methods and allows the decision maker to choose how to give preference information to the system, and enables changing it anytime during the solution process.

Why Use Interactive Multi-Objective Optimization in Chemical Process Design?

Some shortcomings of some widely used basic methods of multiobjective optimization of chemical process design are pointed out and interactive approaches where the role of a decision maker or a designer is emphasized are suggested.

PROJECT Method for Multiobjective Optimization Based on Gradient Projection and Reference Points

A new interactive method for multiobjective programming (MOP) called the PROJECT method, based on the projection of utility function gradients onto the tangent hyperplane of an efficient set and on a new local search procedure that inherits the advantages of the reference-point method to search for the best compromise solution within a local region.

Modified interactive Chebyshev algorithm (MICA) for convex multiobjective programming

The Interactive Surrogate Worth Trade-Off (ISWT) Method for Multiobjective Decision-Making

An interactive and modified version of the Surrogate Worth Trade-off method for multiobjective decision-making is presented, providing a simple and effective procedure by which the DM and the analyst can interactively and systematically explore the Pareto optimal set while trying to maximize the DM's unknown utility function.

Introduction to Multiobjective Optimization: Noninteractive Approaches

This chapter gives an introduction to nonlinear multiobjective optimization by covering some basic concepts as well as outlines of some methods where the decision maker either is not involved or specifies preference information before or after the actual solution process.

The Use of Reference Objectives in Multiobjective Optimization

Any point in the objective space can be used instead of weighting coefficients to derive scalarizing functions which have minima at Pareto points only, and entire basic theory of multiobjective optimization can be developed with the help of reference objectives.

Gradient projection and local region search for multiobjective optimisation

An Overview of Evolutionary Algorithms in Multiobjective Optimization

Current multiobjective evolutionary approaches are discussed, ranging from the conventional analytical aggregation of the different objectives into a single function to a number of population-based approaches and the more recent ranking schemes based on the definition of Pareto optimality.

A Visual Interactive Method for Solving the Multiple-Criteria Problem

...