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interactive algorithms for improving incomplete linguistic preference relations based on consistency measures

APPLIED SOFT COMPUTING 2016年 42卷 66-79页

作者： Wang, Hai Xu, Zeshui Southeast Univ Sch Econ & Management Nanjing 211189 Jiangsu Peoples R China

Incomplete linguistic preference relations (InLPRs) are generally inevitable in group decision making problems due to several reasons. Two vital issues of InLPRs are the consistency and the estimation of missing entries. The initial InLPR may be not consistent, which means that some of its entries do not reflect the real opinions of the experts accurately. Thus, there are deviations between some initial provided values and real opinions. Therefore, it is valuable to elicit the providers to realize and repair the deviations. In this paper, we discuss the consistency and the completing algorithms of InLPRs by interacting with the experts. Servicing as the minimum condition of consistency, the weak consistency of InLPRs is defined and a weak consistency reaching algorithm is designed to guarantee the logical correctness of InLPRs. Then two distinct completing algorithms are presented to estimate the missing entries. The former not only estimates all possible linguistic terms and represents them by the extended hesitant fuzzy linguistic terms sets but also keeps weak consistency during the computing procedures. The later can automatically revise the existing entries using the new opinions supplemented by the experts during interactions. All the proposed algorithms interact with the experts to elicit and mine their actual opinions more accurately. A real case study is also presented to clarify the advantages of our proposal. Moreover, these algorithms can serve as assistant tools for the experts to present their preferences. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Decision analysis Incomplete linguistic preference relation interactive algorithm Consistency measure Hesitant fuzzy linguistic term sets

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INSPM: An interactive evolutionary multi-objective algorithm with preference model

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INFORMATION SCIENCES 2014年 268卷 202-219页

作者： Pedro, Luciana R. Takahashi, Ricardo H. C. Univ Fed Minas Gerais Belo Horizonte MG Brazil Univ Fed Minas Gerais Dep Math Belo Horizonte MG Brazil

In this paper an interactive method for modeling the preferences of a Decision-Maker (DM) is employed to guide a modified version of the NSGA-II algorithm: the interactive Non-dominated Sorting algorithm with Preference Model (INSPM). The INSPM's task is to find a non-uniform sampling of the Pareto-optimal front with a detailed sampling of the DM's preferred regions and a coarse sampling of the non-preferred regions. In the proposed technique, a Radial Basis Function (RBF) network is employed to construct a function which represents the DM's utility function using ordinal information only, extracted from queries to the DM. The INSPM algorithm calls the DM's preference model via a Dynamic Crowding Distance (DCD) density control method which provides the mechanism for increasing the sampling in the preferred regions and for decreasing it in non-preferred regions which allows a fine-tunning control of the Pareto-optimal front sampling density. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Multi-objective optimization Evolutionary computation Utility function model Progressive preference interactive algorithm RBF network

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interactive Depth First algorithm for Oil Transportation Emergency Response Planning 14

Interactive Depth First Algorithm for Oil Transportation Eme...

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Simulation Multiconference

作者： Victor Romanov Ilya Moskovoy Kseniya Grigoryeva Russian Plekhanov University Information Systems in Economics and Management Department

ISBN: (纸本)9781632667335

In the event of an oil spill, an essential element of responding effectively to any adverse consequences is proper planning to define tasks, personnel, and resources necessary. The plan should include coordination with appropriate local, municipal, territorial, regional and federal departments, and include ground and air transportation, logistics support, spill reagent supply and repair equipment delivery. The task of automated planning-based on the concepts and principles of Artificial Intelligence (AI)-has become more urgent due to a dramatic rise in oil leak events. The idea of automated planning is not new. It is based on 50 years of research in the field, including predicate logic, situational calculus, dynamic and integer programming models STRIPS [19], PDDL [22] languages apply. The challenge is especially urgent for the petroleum and gas sectors of the Russian economy as the number of oil leaks has spiked to 28 thousand per year.

关键词： Automated planning Oil spill Pipeline rupture interactive algorithm Restoring plans Situation calculus Depth first search Depth--first search oil spill hazard preparedness Petroleum Transportation Integer programming models

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Many-Objective Virtual Machine Placement

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JOURNAL OF GRID COMPUTING 2017年第2期15卷 161-176页

作者： Lopez-Pires, Fabio Baran, Benjamin Itaipu Technol Pk Hernandarias Paraguay Natl Univ Asuncion San Lorenzo Paraguay

The process of selecting which virtual machines (VMs) should be executed at each physical machine (PM) of a virtualized infrastructure is commonly known as Virtual Machine Placement (VMP). This work presents a general many-objective optimization framework that is able to consider as many objective functions as needed when solving a VMP problem in a pure multi-objective context. As an example of utilization of the proposed framework, a formulation of a many-objective VMP problem (MaVMP) is proposed, considering the simultaneous optimization of the following five objective functions: (1) power consumption, (2) network traffic, (3) economical revenue, (4) quality of service and (5) network load balancing. To solve the formulated MaVMP problem, an interactive memetic algorithm is proposed. Experimental results prove the correctness of the proposed algorithm, its effectiveness converging to a manageable number of solutions and its capabilities to solve problem instances with large numbers of PMs and VMs.

关键词： Virtual machine placement Many-objective optimization Memetic algorithm interactive algorithm Cloud computing Resource allocation

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A class of stochastic optimization problems with one quadratic & several linear objective functions and extended portfolio selection model

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2002年第1期146卷 99-113页

作者： Xu, JP Li, J Sichuan Univ Dept Managing Sci & Engn Chengdu 610065 Peoples R China

In this paper a class of stochastic multiple-objective programming problems with one quadratic, several linear objective functions and linear constraints has been introduced. The former model is transformed into a deterministic multiple-objective nonlinear programming model by means of the introduction of random variables' expectation. The reference direction approach is used to deal with linear objectives and results in a linear parametric optimization formula with a single linear objective function. This objective function is combined with the quadratic function using the weighted sums. The quadratic problem is transformed into a linear (parametric) complementary problem, the basic formula for the proposed approach. The sufficient and necessary conditions for (properly, weakly) efficient solutions and some construction characteristics of (weakly) efficient solution sets are obtained. An interactive algorithm is proposed based on reference direction and weighted sums. Varying the parameter vector on the right-hand side of the model, the DM can freely search the efficient frontier with the model. An extended portfolio selection model is formed when liquidity is considered as another objective to be optimized besides expectation and risk. The interactive approach is illustrated with a practical example. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： (stochastic) multiple quadratic-linear programming interactive algorithm (weakly) efficient solution portfolio selection model

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An interactive Approach to Multiobjective Clustering of Gene Expression Patterns

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2013年第1期60卷 35-41页

作者： Mukhopadhyay, Anirban Maulik, Ujjwal Bandyopadhyay, Sanghamitra Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India

Some recent studies have posed the problem of data clustering as a multiobjective optimization problem, where several cluster validity indices are simultaneously optimized to obtain tradeoff clustering solutions. A number of cluster validity index measures are available in the literature. However, none of the measures can perform equally well in all kinds of datasets. Depending on the dataset properties and its inherent clustering structure, different cluster validity measures perform differently. Therefore, it is important to find the best set of validity indices that should be optimized simultaneously to obtain good clustering results. In this paper, a novel interactive genetic algorithm-based multiobjective approach is proposed that simultaneously finds the clustering solution as well as evolves the set of validity measures that are to be optimized simultaneously. The proposed method interactively takes the input from the human decision maker (DM) during execution and adaptively learns from that input to obtain the final set of validity measures along with the final clustering result. The algorithm is applied for clustering real-life benchmark gene expression datasets and its performance is compared with that of several other existing clustering algorithms to demonstrate its effectiveness. The results indicate that the proposed method outperforms the other existing algorithms for all the datasets considered here.

关键词： Clustering interactive algorithm microarray gene expression multiobjective genetic algorithm Pareto optimality

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interactive multiobjective fuzzy random linear programming: Maximization of possibility and probability

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2008年第2期188卷 530-539页

作者： Katagiri, Hideki Sakawa, Masatoshi Kato, Kosuke Nishizaki, Ichiro Hiroshima Univ Grad Sch Engn Hiroshima 7398527 Japan

This paper considers multiobjective linear programming problems with fuzzy random variables coefficients. A new decision making model is proposed to maximize both possibility and probability, which is based on possibilistic programming and stochastic programming. An interactive algorithm is constructed to obtain a satisficing solution satisfying at least weak Pareto optimality. (c) 2007 Elsevier B.V. All rights reserved.

关键词： fuzzy random variable multiobjective linear programming probability maximization model stochastic programming possibilistic programming interactive algorithm

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Estimating the form of a decision maker's preference function and converging towards preferred solutions

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IISE TRANSACTIONS 2020年第6期52卷 651-664页

作者： Karakaya, Gulsah Koksalan, Murat Middle East Tech Univ Dept Business Adm Ankara Turkey Middle East Tech Univ Dept Ind Engn Ankara Turkey Univ Michigan Ross Sch Business Ann Arbor MI 48109 USA

Preference functions have been widely used to scalarize multiple objectives. Various forms such as linear, quasiconcave, or general monotone have been assumed. In this article, we consider a general family of functions that can take a variety of forms and has properties that allow for estimating the form efficiently. We exploit these properties to estimate the form of the function and converge towards a preferred solution(s). We develop the theory and algorithms to efficiently estimate the parameters of the function that best represent a decision maker's preferences. This in turn facilitates fast convergence to preferred solutions. We demonstrate on a variety of experiments that the algorithms work well both in estimating the form of the preference function and converging to preferred solutions.

关键词： Multiple objective programming interactive algorithm preference function form

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interactive multiobjective fuzzy random programming through the level set-based probability model

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INFORMATION SCIENCES 2011年第9期181卷 1641-1650页

作者： Katagiri, Hideki Sakawa, Masatoshi Hiroshima Univ Grad Sch Engn Hiroshima 7398527 Japan

This paper considers a multiobjective linear programming problem involving fuzzy random variable coefficients. A new fuzzy random programming model is proposed by extending the ideas of level set-based optimality and a stochastic programming model. The original problem involving fuzzy random variables is transformed into a deterministic equivalent problem through the proposed model. An interactive algorithm is provided to obtain a satisficing solution for a decision maker from among a set of newly defined Pareto optimal solutions. It is shown that an optimal solution of the problem to be solved iteratively in the interactive algorithm is analytically obtained by a combination of the bisection method and the simplex method. (C) 2011 Published by Elsevier Inc.

关键词： Fuzzy random variable Multiobjective linear programming Level set-based model Pareto optimality Probability maximization interactive algorithm

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ON A CONSTRUCTIVE APPROXIMATION OF THE EFFICIENT OUTCOMES IN BICRITERION VECTOR OPTIMIZATION

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JOURNAL OF GLOBAL OPTIMIZATION 1994年第1期5卷 35-48页

作者： HELBIG, S JOHANN WOLFGANG GOETHE UNIV FACHBEREICH MATHD-60325 FRANKFURT 1GERMANY

For bicriterion quasiconvex optimization problems, we present a constructive procedure for an approximation of the efficient outcomes. Performing this procedure we can estimate the accuracy of the approximation. Conversely, if we prescribe an accuracy for the approximation, we can calculate the number of points which have to be computed by a certain scalarization method to remain under the given accuracy. Finally, we give a numerical example.

关键词： BICRITERION VECTOR OPTIMIZATION APPROXIMATION OF THE EFFICIENT OUTCOMES interactive algorithm

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