Interactive evolutionary multi-objective optimization for quasi-concave preference functions

Date

2010-10-16

Author

Fowler, John W.
Gel, Esma S.
Köksalan, Mustafa Murat
Korhonen, Pekka
Marquis, Jon L.
Wallenius, Jyrki

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

181
views

0
downloads

We present a new hybrid approach to interactive evolutionary multi-objective optimization that uses a partial preference order to act as the fitness function in a customized genetic algorithm. We periodically send solutions to the decision maker (DM) for her evaluation and use the resulting preference information to form preference cones consisting of inferior solutions. The cones allow its to implicitly rank solutions that the DM has not considered. This technique avoids assuming an exact form for the preference function, but does assume that the preference function is quasi-concave. This paper describes the genetic algorithm and demonstrates its performance on the multi-objective knapsack problem. (C) 2010 Elsevier By. All rights reserved.

Subject Keywords

Interactive optimization , Multi-objective optimization, Evolutionary optimization, Knapsack problem

URI

https://hdl.handle.net/11511/57452

Journal

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

DOI

https://doi.org/10.1016/j.ejor.2010.02.027

Collections

Department of Industrial Engineering, Article

Suggestions

OpenMETU
Core

Adaptive dimensional search: A new metaheuristic algorithm for discrete truss sizing optimization Hasançebi, Oğuzhan (2015-07-01) In the present study a new metaheuristic algorithm called adaptive dimensional search (ADS) is proposed for discrete truss sizing optimization problems. The robustness of the ADS lies in the idea of updating search dimensionality ratio (SDR) parameter online during the search for a rapid and reliable convergence towards the optimum. In addition, several alternative stagnation-control strategies are integrated with the algorithm to escape from local optima, in which a limited uphill (non-improving) move is p...
Improving Computational Efficiency of Bat-Inspired Algorithm in Optimal Structural Design Hasançebi, Oğuzhan (2015-07-01) Bat-inspired (BI) algorithm is a recent metaheuristic optimization technique that simulates echolocation behavior of bats in seeking a design space. Along the same line with almost all metaheuristics, this algorithm also entails a large number of time-consuming structural analyses in structural design optimization applications. This study is focused on improving computational efficiency of the BI algorithm in optimum structural design. The number of structural analyses required by BI algorithm in the course...
An Evolutionary Algorithm for the Multi-objective Multiple Knapsack Problem SOYLU, Banu; Köksalan, Mustafa Murat (2009-06-26) In this study, we consider the multi-objective multiple knapsack problem (MMKP) and we adapt our favorable weight based evolutionary algorithm (FWEA) to approximate the efficient frontier of MMKP. The algorithm assigns fitness to solutions based on their relative strengths as well as their non-dominated frontiers. The relative strength is measured based on a weighted Tchebycheff distance from the ideal point where each Solution chooses its own weights that minimize its distance from the ideal point. We carr...
A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data Karagoz, Gizem Nur; Yazıcı, Adnan; Dokeroglu, Tansel; Coşar, Ahmet (2020-06-01) There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms. Selecting the most appropriate subset of features is a significant problem while maintaining/improving the accuracy of the prediction results. This study proposes a framework of parallel multi-objective Non-dominated Sorting Genetic Algorithms (NSGA-II) for exploring a Pareto set of non-dominated solutions. The subsets of non-dominated featu...
Application of genetic algorithms to geometry optimization of microclusters: A comparative study of empirical potential energy functions for silicon Erkoc, S; Leblebicioğlu, Mehmet Kemal; Halıcı, Uğur (Informa UK Limited, 2003-01-01) Evolutionary computation techniques (in particular, genetic algorithms) have been applied to optimize the structure of microclusters. Various empirical potential energy functions have been used to describe the interactions among the atoms in the clusters. A comparative study of silicon microclusters has been performed.

Citation Formats

J. W. Fowler, E. S. Gel, M. M. Köksalan, P. Korhonen, J. L. Marquis, and J. Wallenius, “Interactive evolutionary multi-objective optimization for quasi-concave preference functions,” EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, pp. 417–425, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57452.