Implementation aspects of interactive multiobjective optimization for modeling environments: the case of GAMS-NIMBUS

Ojalehto, Vesa; Miettinen, Kaisa; Laukkanen, Timo

doi:10.1007/s10589-014-9639-y

Cited by 17 publications

(21 citation statements)

References 43 publications

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“…These solutions are shown to the DM, and he or she can select one of them or one of the previously generated Pareto optimal solutions as the most preferred solution or as a starting solution of a new classication. For a more detailed description of the NIMBUS method, see [16,17,11].…”

Section: The Interactive Nimbus Methodsmentioning

confidence: 99%

“…The agent assisted algorithm proposed can be used with dierent interactive methods following the general core structure of interactive multiobjective optimization methods [11]. The core structure can be described as follows:…”

Section: Interactive Multiobjective Optimizationmentioning

confidence: 99%

“…These problem include optimal control of a continuous casting of steel [4,5], intensity modulated radiotherapy treatment planning [6], optimizing congurations of an oxyfuel power plant process [7], operating wastewater treatment plant [8,9], optimal design and control of a paper mill [10], among others. For more examples of use of interactive methods in various elds see [11] and references therein.…”

Section: Introductionmentioning

confidence: 99%

“…After the DM has found his or her most preferred approximate Pareto optimal solution, as the name of the projection phase suggests, the solution is projected to the Pareto frontier of the original problem. This can be done using, for example, an achievement scalarization function [42] as described in [11].…”

Section: Introduction To the Agent Assisted Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Agent assisted interactive algorithm for computationally demanding multiobjective optimization problems

Ojalehto

Podkopaev

Miettinen

2015

Computers & Chemical Engineering

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Section: The Interactive Nimbus Methodsmentioning

confidence: 99%

Section: Interactive Multiobjective Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction To the Agent Assisted Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Agent assisted interactive algorithm for computationally demanding multiobjective optimization problems

Ojalehto

Podkopaev

Miettinen

2015

Computers & Chemical Engineering

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“…It is implemented in the IND-NIMBUS software platform ( [28,29]) designed for solving industrial problems, as well as in a web-based application WWW-NIMBUS available via the Internet [21]. The NIMBUS method has been applied to a number of engineering problems [11,22,24].…”

Section: Example Implementationmentioning

confidence: 99%

A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

Filatovas

Podkopaev

Kurasova

2015

INT J COMPUT COMMUN

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Interactive methods of multiobjective optimization repetitively derive Pareto optimal solutions based on decision maker's preference information and present the obtained solutions for his/her consideration. Some interactive methods save the obtained solutions into a solution pool and, at each iteration, allow the decision maker considering any of solutions obtained earlier. This feature contributes to the flexibility of exploring the Pareto optimal set and learning about the optimization problem. However, in the case of many objective functions, the accumulation of derived solutions makes accessing the solution pool cognitively difficult for the decision maker. We propose to enhance interactive methods with visualization of the set of solution outcomes using dimensionality reduction and interactive mechanisms for exploration of the solution pool. We describe a proposed visualization technique and demonstrate its usage with an example problem solved using the interactive method NIMBUS.

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Editorial: Special issue on understanding complexity in multiobjective optimization

Greco

Klamroth

Knowles

et al. 2017

J. Multi-Crit. Decis. Anal.

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This special issue of JMCDA was inspired by the work of three Dagstuhl seminars aimed at strengthening the links between the scientific communities of multiple criteria decision making (MCDM) and evolutionary multiobjective optimization. These three Dagstuhl seminars were devoted to the following topics:• Hybrid and robust approaches to multiobjective optimization Mutzel investigate complexity for multiobjective combinatorial optimization problems, taking into consideration output-sensitive complexity of an algorithm for a general enumeration problem, that is, the property that its running time is bounded by a polynomial in the input and the output size. The paper shows that output-sensitive complexity is able to separate efficiently solvable from presumably not efficiently solvable problems, proving also that multiobjective s-t-path problems do not admit an output-sensitive algorithm under weak complexity theoretic assumptions as P ≠ NP. Rodrigo Lankaites Pinheiro, Dario Landa-Silva, and Jason Atkin present a technique that supports understanding the relationships between objectives in a multiobjective optimization problem through a visualization and analysis of the local and global relationships between objectives. The advantages of the proposed technique are shown in experiments on three different combinatorial optimization problems (multiobjective multidimensional knapsack problem, multiobjective nurse scheduling problem, and multiobjective vehicle routing problem with time windows).view of navigation, that is, the interactive procedure of traversing through a set of points (the navigation set) in the objective space guided by a decision maker, with the ultimate goal of identifying the single most preferred Pareto optimal solution. The authors describe a general framework to capture a wide range of navigation methods taking also into account real-world problems to which these methods have been applied and highlighting directions of future research. consider complex systems composed of strongly interrelated subsystems or subproblems with single or multiple objectives that are usually not sequentially ordered or obviously decomposable. In the literature, these systems are also referred to as "interwoven systems" or "systems of systems." Due to the correlation between the components, the overall system performance does not equal the simple sum of their performances, and inclusion of complex synergy may imply possible inaccuracies in the model and prohibitively expensive computations. The authors review recent developments in this field and present a preliminary mathematical model of an interwoven system introducing some approaches to its multiobjective optimization. start from the consideration that despite the fact that in general multiobjective combinatorial optimization problems are known to be hard problems because very often they are NP-complete and intractable, there are also variants or cases of multiobjective combinatorial optimization problems that are easy. The article focuses on particular cases of...

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Implementation aspects of interactive multiobjective optimization for modeling environments: the case of GAMS-NIMBUS

Cited by 17 publications

References 43 publications

Agent assisted interactive algorithm for computationally demanding multiobjective optimization problems

Agent assisted interactive algorithm for computationally demanding multiobjective optimization problems

A Visualization Technique for Accessing Solution Pool in Interactive Methods of Multiobjective Optimization

Editorial: Special issue on understanding complexity in multiobjective optimization

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