Impact of Number of Interactions, Different Interaction Patterns, and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms

ABSTRACT We investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an int...

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Bibliographic Details
Published in:Decision sciences Vol. 46; no. 5; pp. 981 - 1006
Main Authors: Marquis, Jon, Gel, Esma S., Fowler, John W., Köksalan, Murat, Korhonen, Pekka, Wallenius, Jyrki
Format: Journal Article
Language:English
Published: Atlanta Blackwell Publishing Ltd 01.10.2015
American Institute for Decision Sciences
Subjects:
Algorithms
Convergence
Decision analysis
Decisions
Evolutionary Multiobjective Optimization (EMO)
Human technology relationship
Human-computer interaction
Inconsistency
Interactive
Internet
Interpersonal communication
Knapsack problem
Location analysis
Multiple criteria decision making
Optimization
Optimization algorithms
Preference Function
Preferences
Robustness
Studies
ISSN:0011-7315, 1540-5915
Online Access:Get full text
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Summary:ABSTRACT We investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an interaction” means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.
Bibliography:ArticleID:DECI12180
ark:/67375/WNG-L331TXNW-Q
Wallenius wishes to acknowledge the financial support from the Academy of Finland (grant number 13253583) and the KAUTE Foundation, Finland.
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ISSN:0011-7315
1540-5915
DOI:10.1111/deci.12180