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Ruben Saborido - One of the best experts on this subject based on the ideXlab platform.

  • ira emo interactive Method using reservation and aspiration levels for evolutionary Multiobjective Optimization
    International Conference on Evolutionary Multi-criterion Optimization, 2019
    Co-Authors: Ruben Saborido, Mariano Luque, Ana Belen Ruiz, K. Miettinen
    Abstract:

    We propose a new interactive evolutionary Multiobjective Optimization Method, IRA-EMO. At each iteration, the decision maker (DM) expresses her/his preferences as an interesting interval for objective function values. The DM also specifies the number of representative Pareto optimal solutions in these intervals referred to as regions of interest one wants to study. Finally, a real-life engineering three-objective Optimization problem is used to demonstrate how IRA-EMO works in practice for finding the most preferred solution.

  • an interactive evolutionary Multiobjective Optimization Method interactive wasf ga
    International Conference on Evolutionary Multi-criterion Optimization, 2015
    Co-Authors: Ana Belen Ruiz, K. Miettinen, Mariano Luque, Ruben Saborido
    Abstract:

    In this paper, we describe an interactive evolutionary algorithm called Interactive WASF-GA to solve Multiobjective Optimization problems. This algorithm is based on a preference-based evolutionary Multiobjective Optimization algorithm called WASF-GA. In Interactive WASF-GA, a decision maker provides preference information at each iteration simply as a reference point consisting of desirable objective function values and the number of solutions to be compared. Using this information, the desired number of solutions is generated to represent the region of interest of the Pareto optimal front associated to the reference point given. Interactive WASF-GA implies a much lower computational cost than the original WASF-GA because it generates a small number of solutions. This speeds up the convergence of the algorithm, making it suitable for many decision-making problems. Its efficiency and usefulness is demonstrated with a five-objective Optimization problem.

K. Miettinen - One of the best experts on this subject based on the ideXlab platform.

  • ira emo interactive Method using reservation and aspiration levels for evolutionary Multiobjective Optimization
    International Conference on Evolutionary Multi-criterion Optimization, 2019
    Co-Authors: Ruben Saborido, Mariano Luque, Ana Belen Ruiz, K. Miettinen
    Abstract:

    We propose a new interactive evolutionary Multiobjective Optimization Method, IRA-EMO. At each iteration, the decision maker (DM) expresses her/his preferences as an interesting interval for objective function values. The DM also specifies the number of representative Pareto optimal solutions in these intervals referred to as regions of interest one wants to study. Finally, a real-life engineering three-objective Optimization problem is used to demonstrate how IRA-EMO works in practice for finding the most preferred solution.

  • an interactive evolutionary Multiobjective Optimization Method interactive wasf ga
    International Conference on Evolutionary Multi-criterion Optimization, 2015
    Co-Authors: Ana Belen Ruiz, K. Miettinen, Mariano Luque, Ruben Saborido
    Abstract:

    In this paper, we describe an interactive evolutionary algorithm called Interactive WASF-GA to solve Multiobjective Optimization problems. This algorithm is based on a preference-based evolutionary Multiobjective Optimization algorithm called WASF-GA. In Interactive WASF-GA, a decision maker provides preference information at each iteration simply as a reference point consisting of desirable objective function values and the number of solutions to be compared. Using this information, the desired number of solutions is generated to represent the region of interest of the Pareto optimal front associated to the reference point given. Interactive WASF-GA implies a much lower computational cost than the original WASF-GA because it generates a small number of solutions. This speeds up the convergence of the algorithm, making it suitable for many decision-making problems. Its efficiency and usefulness is demonstrated with a five-objective Optimization problem.

  • Interactive Multiobjective Optimization with NIMBUS for decision making under uncertainty
    OR Spectrum, 2014
    Co-Authors: K. Miettinen, Jyri Mustajoki, Theodor J. Stewart
    Abstract:

    We propose an interactive Method for decision making under uncertainty, where uncertainty is related to the lack of understanding about consequences of actions. Such situations are typical, for example, in design problems, where a decision maker has to make a decision about a design at a certain moment of time even though the actual consequences of this decision can be possibly seen only many years later. To overcome the difficulty of predicting future events when no probabilities of events are available, our Method utilizes groupings of objectives or scenarios to capture different types of future events. Each scenario is modeled as a Multiobjective Optimization problem to represent different and conflicting objectives associated with the scenarios. We utilize the interactive classification-based Multiobjective Optimization Method NIMBUS for assessing the relative optimality of the current solution in different scenarios. This information can be utilized when considering the next step of the overall solution process. Decision making is performed by giving special attention to individual scenarios. We demonstrate our Method with an example in portfolio Optimization.

  • Interactive Solution Approach to a Multiobjective Optimization Problem in a Paper Machine Headbox Design
    Journal of Optimization Theory and Applications, 2003
    Co-Authors: J.p. Hämäläinen, K. Miettinen, P. Tarvainen, J. Toivanen
    Abstract:

    A successful application of the interactive Multiobjective Optimization Method NIMBUS to a design problem in papermaking technology is described. Namely, an optimal shape design problem related to the paper machine headbox is studied. First, the NIMBUS Method, the numerical headbox model, and the associated Multiobjective Optimization problem are described. Then, the results of numerical experiments are presented.

Shigenobu Kobayashi - One of the best experts on this subject based on the ideXlab platform.

  • local search for Multiobjective function Optimization pareto descent Method
    Genetic and Evolutionary Computation Conference, 2006
    Co-Authors: Ken Harada, Jun Sakuma, Shigenobu Kobayashi
    Abstract:

    Genetic Algorithm (GA) is known as a potent Multiobjective Optimization Method, and the effectiveness of hybridizing it with local search (LS) has recently been reported in the literature. However, there is a relatively small number of studies on LS Methods for Multiobjective function Optimization. Although each of the existing LS Methods has some strong points, they have respective drawbacks such as high computational cost and inefficiency in improving objective functions. Hence, a more effective and efficient LS Method is being sought, which can be used to enhance the performance of the hybridization.Defining Pareto descent directions as descent directions to which no other descent directions are superior in improving all objective functions, this paper proposes a new LS Method, Pareto Descent Method (PDM), which finds Pareto descent directions and moves solutions in such directions thereby improving all objective functions simultaneously. In the case part or all of them are infeasible, it finds feasible Pareto descent directions or descent directions as appropriate. PDM finds these directions by solving linear programming problems, which is computationally inexpensive. Experiments have shown PDM's superiority over existing Methods.

  • local search for Multiobjective function Optimization pareto descent Method
    Transactions of The Japanese Society for Artificial Intelligence, 2006
    Co-Authors: Ken Harada, Jun Sakuma, Kokolo Ikeda, Isao Ono, Shigenobu Kobayashi
    Abstract:

    Many real-world problems entail multiple conflicting objectives, which makes Multiobjective Optimization an important subject. Much attention has been paid to Genetic Algorithm (GA) as a potent Multiobjective Optimization Method, and the effectiveness of its hybridization with local search (LS) has recently been reported in the literature. However, there have been a relatively small number of studies on LS Methods for Multiobjective function Optimization. Although each of the existing LS Methods has some strong points, they have respective drawbacks such as high computational cost and inefficiency of improving objective functions. Hence, a more effective and efficient LS Method is being sought, which can be used to enhance the performance of the hybridization. Pareto descent directions are defined in this paper as descent directions to which no other descent directions are superior in improving all objective functions. Moving solutions in such directions is expected to maximally improve all objective functions simultaneously. This paper proposes a new LS Method, Pareto Descent Method (PDM), which finds Pareto descent directions and moves solutions in such directions. In the case part or all of them are infeasible, it finds feasible Pareto descent directions or descent directions as necessary and moves solutions in these directions. PDM finds these directions by solving linear programming problems. Thus, it is computationally inexpensive. Experiments have shown that PDM is superior to existing Methods.

Ana Belen Ruiz - One of the best experts on this subject based on the ideXlab platform.

  • ira emo interactive Method using reservation and aspiration levels for evolutionary Multiobjective Optimization
    International Conference on Evolutionary Multi-criterion Optimization, 2019
    Co-Authors: Ruben Saborido, Mariano Luque, Ana Belen Ruiz, K. Miettinen
    Abstract:

    We propose a new interactive evolutionary Multiobjective Optimization Method, IRA-EMO. At each iteration, the decision maker (DM) expresses her/his preferences as an interesting interval for objective function values. The DM also specifies the number of representative Pareto optimal solutions in these intervals referred to as regions of interest one wants to study. Finally, a real-life engineering three-objective Optimization problem is used to demonstrate how IRA-EMO works in practice for finding the most preferred solution.

  • an interactive evolutionary Multiobjective Optimization Method interactive wasf ga
    International Conference on Evolutionary Multi-criterion Optimization, 2015
    Co-Authors: Ana Belen Ruiz, K. Miettinen, Mariano Luque, Ruben Saborido
    Abstract:

    In this paper, we describe an interactive evolutionary algorithm called Interactive WASF-GA to solve Multiobjective Optimization problems. This algorithm is based on a preference-based evolutionary Multiobjective Optimization algorithm called WASF-GA. In Interactive WASF-GA, a decision maker provides preference information at each iteration simply as a reference point consisting of desirable objective function values and the number of solutions to be compared. Using this information, the desired number of solutions is generated to represent the region of interest of the Pareto optimal front associated to the reference point given. Interactive WASF-GA implies a much lower computational cost than the original WASF-GA because it generates a small number of solutions. This speeds up the convergence of the algorithm, making it suitable for many decision-making problems. Its efficiency and usefulness is demonstrated with a five-objective Optimization problem.

Jyrki Wallenius - One of the best experts on this subject based on the ideXlab platform.

  • An Interactive Evolutionary Multiobjective Optimization Method Based on Progressively Approximated Value Functions
    IEEE Transactions on Evolutionary Computation, 2010
    Co-Authors: Ankur Sinha, Pekka J. Korhonen, Jyrki Wallenius
    Abstract:

    This paper suggests a preference-based Methodology, which is embedded in an evolutionary Multiobjective Optimization algorithm to lead a decision maker (DM) to the most preferred solution of her or his choice. The progress toward the most preferred solution is made by accepting preference based information progressively from the DM after every few generations of an evolutionary Multiobjective Optimization algorithm. This preference information is used to model a strictly monotone value function, which is used for the subsequent iterations of the evolutionary Multiobjective Optimization (EMO) algorithm. In addition to the development of the value function which satisfies DM's preference information, the proposed progressively interactive EMO-approach utilizes the constructed value function in directing EMO algorithm's search to more preferred solutions. This is accomplished using a preference-based domination principle and utilizing a preference-based termination criterion. Results on two- to five-objective Optimization problems using the progressively interactive NSGA-II approach show the simplicity of the proposed approach and its future promise. A parametric study involving the algorithm's parameters reveals interesting insights of parameter interactions and indicates useful parameter values. A number of extensions to this paper are also suggested.