Interactive Evolutionary Multiple Objective Optimization for Group Decision Incorporating Value-based Preference Disaggregation Methods

Kadziński, Miłosz; Tomczyk, Michał K.

doi:10.1007/s10726-016-9506-6

Cited by 20 publications

(5 citation statements)

References 27 publications

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“…We recognize that the application of some effective heuristics could help solving big instances of portfolio problems. In this regard, we recommend the use of some Evolutionary Multi-objective Optimization algorithms guided by preference information supplied by the DM (see, e.g., [9,10,37,38]).…”

Section: Resultsmentioning

confidence: 99%

Optimization of multiple satisfaction levels in portfolio decision analysis

Barbati

Greco

Kadziński

et al. 2018

Omega

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Section: Resultsmentioning

confidence: 99%

Optimization of multiple satisfaction levels in portfolio decision analysis

Barbati

Greco

Kadziński

et al. 2018

Omega

Self Cite

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“…The NEMO-GROUP, a set of interactive evolutionary multi-objective optimization (MOO) methods, was developed by Kadzinski and Tomczyk in [34]. In these approaches, an evolutionary algorithm is modified with the introduction of pairwise comparisons of several DMs.…”

Section: An Overview Of Gdm-mop Literaturementioning

confidence: 99%

A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

et al. 2021

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This paper addresses group multi-objective optimization under a new perspective. For each point in the feasible decision set, satisfaction or dissatisfaction from each group member is determined by a multi-criteria ordinal classification approach, based on comparing solutions with a limiting boundary between classes “unsatisfactory” and “satisfactory”. The whole group satisfaction can be maximized, finding solutions as close as possible to the ideal consensus. The group moderator is in charge of making the final decision, finding the best compromise between the collective satisfaction and dissatisfaction. Imperfect information on values of objective functions, required and available resources, and decision model parameters are handled by using interval numbers. Two different kinds of multi-criteria decision models are considered: (i) an interval outranking approach and (ii) an interval weighted-sum value function. The proposal is more general than other approaches to group multi-objective optimization since (a) some (even all) objective values may be not the same for different DMs; (b) each group member may consider their own set of objective functions and constraints; (c) objective values may be imprecise or uncertain; (d) imperfect information on resources availability and requirements may be handled; (e) each group member may have their own perception about the availability of resources and the requirement of resources per activity. An important application of the new approach is collective multi-objective project portfolio optimization. This is illustrated by solving a real size group many-objective project portfolio optimization problem using evolutionary computation tools.

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“…The second phase transforms the categorized solutions into preference model parameters. Both phases correspond, respectively, with the indirect and direct elicitation of preferences mentioned in [33,34]. Finally, the third phase incorporates preferences in the solution process as the parameters of the preference model, supporting a multi-criteria classifier.…”

Section: Description Of P-hmcsgamentioning

confidence: 99%

Effect of the Profile of the Decision Maker in the Search for Solutions in the Decision-Making Process

Perez-Villafuerte

Cruz-Reyes

Rangel-Valdez

et al. 2021

MCA

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Many real-world optimization problems involving several conflicting objective functions frequently appear in current scenarios and it is expected they will remain present in the future. However, approaches combining multi-objective optimization with the incorporation of the decision maker’s (DM’s) preferences through multi-criteria ordinal classification are still scarce. In addition, preferences are rarely associated with a DM’s characteristics; the preference selection is arbitrary. This paper proposes a new hybrid multi-objective optimization algorithm called P-HMCSGA (preference hybrid multi-criteria sorting genetic algorithm) that allows the DM’s preferences to be incorporated in the optimization process’ early phases and updated into the search process. P-HMCSGA incorporates preferences using a multi-criteria ordinal classification to distinguish solutions as good and bad; its parameters are determined with a preference disaggregation method. The main feature of P-HMCSGA is the new method proposed to associate preferences with the characterization profile of a DM and its integration with ordinal classification. This increases the selective pressure towards the desired region of interest more in agreement with the DM’s preferences specified in realistic profiles. The method is illustrated by solving real-size multi-objective PPPs (project portfolio problem). The experimentation aims to answer three questions: (i) To what extent does allowing the DM to express their preferences through a characterization profile impact the quality of the solution obtained in the optimization? (ii) How sensible is the proposal to different profiles? (iii) How much does the level of robustness of a profile impact the quality of final solutions (this question is related with the knowledge level that a DM has about his/her preferences)? Concluding, the proposal fulfills several desirable characteristics of a preferences incorporation method concerning these questions.

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Interactive Evolutionary Multiple Objective Optimization for Group Decision Incorporating Value-based Preference Disaggregation Methods

Cited by 20 publications

References 27 publications

Optimization of multiple satisfaction levels in portfolio decision analysis

Optimization of multiple satisfaction levels in portfolio decision analysis

A New Approach to Group Multi-Objective Optimization under Imperfect Information and Its Application to Project Portfolio Optimization

Effect of the Profile of the Decision Maker in the Search for Solutions in the Decision-Making Process

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