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Interactive Evolutionary Multiobjective Optimization via Learning to Rank

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第4期27卷 749-763页

作者： Li, Ke Lai, Guiyu Yao, Xin Univ Elect Sci & Technol China Coll Comp Sci & Engn Chengdu 611731 Peoples R China Univ Exeter Dept Comp Sci NorthPark Rd Exeter EX4 4QF England Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

In practical multicriterion decision making, it is cumbersome if a decision maker (DM) is asked to choose among a set of tradeoff alternatives covering the whole Pareto-optimal front. This is a paradox in conventional evolutionary multiobjective optimization (EMO) that always aim to achieve a well balance between convergence and diversity. In essence, the ultimate goal of multiobjective optimization is to help a DM identify solution(s) of interest (SOI) achieving satisfactory tradeoffs among multiple conflicting criteria. Bearing this in mind, this article develops a framework for designing preference-based EMO algorithms to find SOI in an interactive manner. Its core idea is to involve human in the loop of EMO. After every several iterations, the DM is invited to elicit her feedback with regard to a couple of incumbent candidates. By collecting such information, her preference is progressively learned by a learning-to-rank neural network and then applied to guide the baseline EMO algorithm. Note that this framework is so general that any existing EMO algorithm can be applied in a plug-in manner. Experiments on 48 benchmark test problems with up to ten objectives and a real-world multiobjective robot control problem fully demonstrate the effectiveness of our proposed algorithms for finding SOI.

关键词： Evolutionary multiobjective optimization (EMO) gradient descent learning to rank (LTR) preference modeling

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A simulation optimization method that considers uncertainty and multiple performance measures

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第1期181卷 315-330页

作者： Rosen, Scott L. Harmonosky, Catherine M. Traband, Mark T. Penn State Univ Harold & Inge Marcus Dept Ind & Mfg Engn University Pk PA 16802 USA Appl Res Lab University Pk PA 16802 USA

Simulation optimization provides a structured approach to system design and configuration when analytical expressions for input/output relationships are unavailable. This research focuses on the development of a new simulation optimization technique applicable to systems having multiple performance measures. The aim of this research is to incorporate a simulation end user's preference towards risk and uncertainty into the search process for the best decision alternative. Automation of the optimization procedure is a necessity. Therefore, this paper proposes a simulation optimization method that involves a preference model, specifically adapted for decision making with simulation models. The proposed simulation optimization method is evaluated against two simulation optimization methods with embedded deterministic, multiple criteria decision making strategies. It is shown on average to obtain significantly better solutions in multiple types of experimental settings having normally distributed simulation performance measures. (c) 2006 Elsevier B.V. All rights reserved.

关键词： simulation simulation optimization MCDM preference modeling

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On efficient WOWA optimization for decision support under risk

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2009年第6期50卷 915-928页

作者： Ogryczak, Wlodzimierz Sliwinski, Tomasz Warsaw Univ Technol Fac Elect & IT Inst Control & Computat Engn PL-00665 Warsaw Poland

The problem of averaging outcomes under several scenarios to form overall objective functions is of considerable importance in decision support under uncertainty. The so-called Weighted OWA (WOWA) aggregation offers a well-suited approach to this problem. The WOWA aggregation, similar to the classical ordered weighted averaging (OWA). uses the preferential weights assigned to the ordered values (i.e. to the worst value, the second worst and so on) rather than to the specific criteria. This allows one to model various preferences with respect to the risk. Simultaneously, importance weighting of scenarios can be introduced. In this paper, we analyze solution procedures for optimization problems with the WOWA objective functions related to decisions under risk. Linear programming formulations are introduced for optimization of the WOWA objective with monotonic preferential weights thus representing risk averse preferences. Their computational efficiency is demonstrated. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Decision under risk preference modeling Aggregation operators OWA WOWA Linear programming

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A valued Ferrers relation for interval comparison

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FUZZY SETS AND SYSTEMS 2015年 266卷 47-66页

作者： Oeztuerk, Meltem Tsoukias, Alexis Univ Paris 09 CNRS Lamsade Paris France

The paper deals with the valued comparison of intervals for decision making. Interval orders are classical preference structures where the comparison of intervals is done in an ordinal way. In this paper we focus on valued comparison where more information, especially the distance between end-points of intervals, is used in order to have more sophisticated preference structures. The generalization of an interval order as a valued structure requires the choice of de Morgan triplets. We propose a valued outranking relation for interval comparison and show that it satisfies different definitions of valued interval orders using different de Morgan triplet. The decomposition of our outranking relation into preference and indifference provides a valued preference structure where the preference is T-transitive and monotone. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Decision making preference modeling Valued binary relation Interval comparison Interval orders Ferrer relation

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Representing perceived tradeoffs in defining disaster resilience

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DECISION SUPPORT SYSTEMS 2011年第2期50卷 394-403页

作者： Zobel, Christopher W. Virginia Polytech Inst & State Univ Dept Business Informat Technol Blacksburg VA 24061 USA

Two of the primary measures that characterize the concept of disaster resilience are the initial impact of a disaster event and the subsequent time to recovery. This paper presents a new analytic approach to representing the relationship between these two characteristics by extending a multi-dimensional approach for predicting resilience into a technique for fitting the resilience function to the preferences and priorities of a given decision maker. This allows for a more accurate representation of the perceived value of different resilience scenarios to that individual, and thus makes the concept more relevant in the context of strategic decision making. Published by Elsevier B.V.

关键词： Adjusted resilience Optimization Visualization Tradeoffs Decision support preference modeling

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Axiomatic characterization of a general utility function and its particular cases in terms of conjoint measurement and rough-set decision rules

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2004年第2期158卷 271-292页

作者： Greco, S Matarazzo, B Slowinski, R Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland Univ Catania Fac Econ I-95129 Catania Italy Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

Utility or value functions play an important role of preference models in multiple-criteria decision making. We investigate the relationships between these models and the decision-rule preference model obtained from the Dominance-based Rough Set Approach. The relationships are established by means of special "cancellation properties" used in conjoint measurement as axioms for representation of aggregation procedures. We are considering a general utility function and three of its important special cases: associative operator, Sugeno integral and ordered weighted maximum. For each of these aggregation functions we give a representation theorem establishing equivalence between a very weak cancellation property, the specific utility function and a set of rough-set decision rules. Each result is illustrated by a simple example of multiple-criteria decision making. The results show that the decision rule model we propose has clear advantages over a general utility function and its particular cases. (C) 2003 Elsevier B.V. All rights reserved.

关键词： multiple-criteria decision making preference modeling utility function conjoint measurement decision rules axiomatization

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Inferring Individual Human Mobility From Sparse Check-in Data: A Temporal-Context-Aware Approach

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第1期11卷 600-611页

作者： Xu, Shuai Fu, Xiaoming Pi, Dechang Ma, Zhuo Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Univ Gottingen Inst Comp Sci D-37077 Gottingen Germany Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Jiangsu Police Inst Dept Comp Informat & Cyber Secur Nanjing 210031 Peoples R China

Inferring individual human mobility at a given time is not only beneficial for personalized location-based services but also crucial for tracking trajectory of the confirmed cases in the COVID-19 pandemic. However, individual-generated trajectory data from mobile Apps are characterized by implicit feedback, which means only a few individual-location interactions can be observed. Existing studies based on such sparse trajectory data are not sufficient to infer an individual's missing mobility in his/her historical trajectory and further predict an individual's future mobility at a given time under a unified framework. To address this concern, in this article, we propose a temporal-context-aware framework that incorporates multiple factors to model the time-sensitive individual-location interactions in a bottom-up way. Based on the idea of feature fusion, the driving effect of heterogeneous information on an individual's mobility is gradually strengthened, so that the temporal-spatial context when a check-in occurs can be accurately perceived. We leverage Bayesian personalized ranking (BPR) to optimize the model, where a novel negative sampling method is employed to alleviate data sparseness. Based on three real-world datasets, we evaluate the proposed approach with regard to two different tasks, namely, missing mobility inference and future mobility prediction at a given time. Experimental results encouragingly demonstrate that our approach outperforms multiple baselines in terms of two evaluation metrics. Furthermore, the predictability of individual mobility within different time windows is also revealed.

关键词： Context perception feature fusion individual human mobility location prediction preference modeling

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Ranking Intervals and Dominance Relations for Ratio-Based Efficiency Analysis

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MANAGEMENT SCIENCE 2011年第1期57卷 200-214页

作者： Salo, Ahti Punkka, Antti Aalto Univ Sch Sci Syst Anal Lab Aalto 00076 Finland

We develop comparative results for ratio-based efficiency analysis (REA) based on the decision-making units' (DMUs') relative efficiencies over sets of feasible weights that characterize preferences for input and output variables. Specifically, we determine (i) ranking intervals, which indicate the best and worst efficiency rankings that a DMU can attain relative to other DMUs;(ii) dominance relations, which show what other DMUs a given DMU dominates in pairwise efficiency comparisons;and (iii) efficiency bounds, which show how much more efficient a given DMU can be relative to some other DMU or a subset of other DMUs. Unlike conventional efficiency scores, these results are insensitive to outlier DMUs. They also show how the DMUs' efficiency ratios relate to each other for all feasible weights, rather than for those weights only for which the data envelopment analysis (DEA) efficiency score of some DMU is maximized. We illustrate the usefulness of these results by revisiting reported DEA studies and by describing a recent case study on the efficiency comparison of university departments.

关键词： efficiency analysis data envelopment analysis preference modeling

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Comprehensive Rules-Based and preferences Induced Weights Allocation in Group Decision-Making with BUI

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-8页

作者： Li, GePeng Yager, Ronald R. Zhang, XinXing Mesiar, Radko Bustince, Humberto Jin, LeSheng Nanjing Normal Univ Sch Business Nanjing Peoples R China King Abdulaziz Univ Fac Sci Jeddah Saudi Arabia Iona Coll Inst Machine Intelligence New Rochelle NY 10801 USA Slovak Univ Technol Bratislava Fac Civil Engn Radlinskeho 11 SK-81005 Bratislava Slovakia Palacky Univ Dept Algebra & Geometry Fac Sci 17 Listopadu 12 Olomouc 77146 Czech Republic Univ Publ Navarra Dept Stat Comp Sci & Math Campus Arrosadia Sn Pamplona 31006 Spain

Decision-makers' subjective preferences can be well modeled using preference aggregation operators and related induced weights allocation mechanisms. However, when several different types of preferences occur in some decision environment with more complex uncertainties, repeated uses of preferences induced weights allocation sometimes become unsuitable or less reasonable. In this work, we discuss a common decision environment where several invited experts will offer their respective evaluation values for a certain object. There are three types of preferences which will significantly affect the weights allocations from experts. Instead of unsuitably performing preference induced weights allocation three times independently and then merging the results together using convex combination as some literatures recently did, in this work, we propose some organic and comprehensive rules-based screen method to first rule out some unqualified experts and then take preference induced weights allocation for the refined group of experts. A numerical example in business management and decision-making is presented to show the cognitive reasonability and practical feasibility.

关键词： Aggregation operators Basic uncertain information Decision-making Information fusion preference modeling

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Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method

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INFORMATION SCIENCES 2011年第1期181卷 44-56页

作者： Fernandez, Eduardo Lopez, Edy Lopez, Fernando Coello Coello, Carlos A. Autonomous Univ Sinaloa Fac Ingn Culiacan 80040 Mexico

Most current approaches in the evolutionary multiobjective optimization literature concentrate on adapting an evolutionary algorithm to generate an approximation of the Pareto frontier. However, finding this set does not solve the problem. The decision-maker still has to choose the best compromise solution out of that set. Here, we introduce a new characterization of the best compromise solution of a multiobjective optimization problem. By using a relational system of preferences based on a multicriteria decision aid way of thinking, and an outranked-based dominance generalization, we derive some necessary and sufficient conditions which describe satisfactory approximations to the best compromise. Such conditions define a lexicographic minimum of a bi-objective optimization problem, which is a map of the original one. The NOSGA-II method is a NSGA-II inspired efficient way of solving the resulting mapped problem. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Evolutionary algorithms Multiobjective optimization Multicriteria decision preference modeling

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