Evolutionary multiobjective optimization using an outranking-based dominance generalization

Fernández, Eduardo; Lopez, Edy; Bernal, Sergio; Coello, Carlos A. Coello; Navarro, Jorge

doi:10.1016/j.cor.2009.06.004

Cited by 54 publications

(28 citation statements)

References 11 publications

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“…Secondly the information acquired from the DM is quite difficult to be defined, and lastly this type of preference requires a lot of effort in reasoning activity to construct the required information [21]. Moreover, direct preference may cause difficulty to create the DM judgment if the size of criteria increases [26]. MCDA helps the DM in making good decisions by dividing or aggregating all the possible evaluations criteria into relevant evaluations criteria.…”

Section: Multi-criteria Decision Analysis (Mcda)mentioning

confidence: 99%

A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

Mohamad

Selamat

Krejcar

et al. 2015

Computational Collective Intelligence

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Abstract. Rough set theory (RST) is one of the data mining tools, which have many capabilities such as to minimize the size of an input data and to produce sets of decision rules from a set of data. RST is also one of the great techniques used in dealing with ambiguity and uncertainty of datasets. It was introduced by Z. Pawlak in 1997 and until now, there are many researchers who really make use of its advantages either to make an enhancement of the RST or to apply in various research areas such as in decision analysis, pattern recognition, machine learning, intelligent systems, inductive reasoning, data preprocessing, knowledge discovery, and expert systems. This paper presents a recent study on the elementary concepts of RST and its implementation in the multi-criteria decision analysis (MCDA) problems.

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Section: Multi-criteria Decision Analysis (Mcda)mentioning

confidence: 99%

A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

Mohamad

Selamat

Krejcar

et al. 2015

Computational Collective Intelligence

View full text Add to dashboard Cite

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“…The main economic and mathematical models to the portfolio problem assume that there is a defined set of n projects, each project well characterized with costs and revenues, of which the distribution over time is known. The Decision Maker (DM) is responsible for selecting the portfolio that the company will implement [11].…”

Section: Portfolio Problemmentioning

confidence: 99%

A Survey of Grey Systems Applied to Multi-objective Problem

Balderas

Fernández

Gómez

et al. 2015

Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

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Decision-making processes involve a series of steps: identifying the problems, constructing the preferences, evaluating the alternatives, and determining the best alternatives (Simon, The new science of management decision. In: Proceedings of the 33rd Conference of theOperational Research Society of New Zealand, Citeseer, 1960). Using a multi-criteria decision making approach, the grey system theory has been used to capture the complexity inherent in selection process. The grey system theory proposed by Deng (J. Grey Syst. 1(1), 1-24 1989) is based on the assumption that a system is uncertain and that the information regarding the system is insufficient to build a relational analysis or to construct a model to characterize the system. The aim of this chapter is to provide a short review of the three mostly seen research methods employed for the investigation of uncertain systems: probability and statistics, fuzzy mathematics and grey systems theory. This chapter provides a short review of the general framework, current research trends and future research topics on grey systems applied to decision problems.

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“…6.Those in which the DM supplies the model's parameters to build a fuzzy outranking relation (e.g. [15,56]). 7.The construction of a desirability function which is based on the assignment of some desirability thresholds (e.g.…”

Section: A Brief Outline and Some Criticisms Of Previous Approachesmentioning

confidence: 99%

Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

Cruz¹,

Fernández²,

Gómez³

et al. 2014

Appl. Math. Inf. Sci.

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Project portfolio selection is one of the most important problems faced by any organization. The decision process involves multiple conflicting criteria, and has been commonly addressed by implementing a two-phase procedure. The first step identifies the efficient solution set; the second step supports the decision maker in selecting only one portfolio solution from the efficient set. However, several recent studies show the advantages gained by optimizing towards a region of interest (according to the decision maker's preferences) instead of approximating the complete Pareto set. However, these works have not faced synergism and its variants, such as cannibalization and redundancy. In this paper we introduce a new approach called Non-Outranked Ant Colony Optimization, which optimizes interdependent project portfolios with a priori articulation of decision-maker preferences based on an outranking model. Several experimental tests show the advantages of our proposal over the two-phase approach, providing reasonable evidence of its potential for solving real-world high-scale problems with many objectives.

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Evolutionary multiobjective optimization using an outranking-based dominance generalization

Cited by 54 publications

References 11 publications

A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

A Survey of Grey Systems Applied to Multi-objective Problem

Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

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