Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Preference-based flexible multiobjective evolutionary algorithms
Download
index.pdf
Date
2008
Author
Karahan, İbrahim
Metadata
Show full item record
Item Usage Stats
173
views
137
downloads
Cite This
In this study,we develop an elitist multiobjective evolutionary algorithm for approximating the Pareto-optimal frontiers of multiobjective optimization problems. The algorithm converges the true Pareto-optimal frontier while keeping the solutions in the population well-spread over the frontier. Diversity of the solutions is maintained by the territory dening property of the algorithm rather than using an explicit diversity preservation mechanism. This leads to substantial computational eciency. We test the algorithm on commonly used test problems and compare its performance against well-known benchmark algorithms. In addition to approximating the entire Pareto-optimal frontier,we develop a preference incorporation mechanism to guide the search towards the decision maker’s regions of interest. Based on this mechanism, we implement two variants of the algorithm. The rst gathers all preference information before the optimization stage to nd approximations of the desired regions. The second one is an interactive algorithm that focuses on the desired region by interacting with the decision maker during the solution process. Based on tests on 2- and 3-objective problems, we observe that both algorithms converge to the preferred regions.
Subject Keywords
Management of Engineering Works.
,
Industrial engineering.
URI
http://etd.lib.metu.edu.tr/upload/12609578/index.pdf
https://hdl.handle.net/11511/18261
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Interval priority weight generation from interval comparison matrices in analytic hierarchy process
Öztürk, Ufuk; Karasakal, Esra; Department of Industrial Engineering (2009)
In this study, for the well-known Analytic Hierarchy Process (AHP) method a new approach to interval priority weight generation from interval comparison matrix is proposed. This method can be used for both inconsistent and consistent matrices. Also for the problems having more than two hierarchical levels a synthesizing heuristic is presented. The performances of the methods, interval generation and synthesizing, are compared with the methods that are already available in the literature on randomly generate...
Optimization of time-cost-resource trade-off problems in project scheduling using meta-heuristic algorithms
Bettemir, Önder Halis; Sönmez, Rifat; Department of Civil Engineering (2009)
In this thesis, meta-heuristic algorithms are developed to obtain optimum or near optimum solutions for the time-cost-resource trade-off and resource leveling problems in project scheduling. Time cost trade-off, resource leveling, single-mode resource constrained project scheduling, multi-mode resource constrained project scheduling and resource constrained time cost trade-off problems are analyzed. Genetic algorithm simulated annealing, quantum simulated annealing, memetic algorithm, variable neighborhood ...
Multi-objective combinatorial optimization using evolutionary algorithms
Özsayın, Burcu; Köksalan, Murat; Department of Industrial Engineering (2009)
Due to the complexity of multi-objective combinatorial optimization problems (MOCO), metaheuristics like multi-objective evolutionary algorithms (MOEA) are gaining importance to obtain a well-converged and well-dispersed Pareto-optimal frontier approximation. In this study, of the well-known MOCO problems, single-dimensional multi-objective knapsack problem and multi-objective assignment problem are taken into consideration. We develop a steady-state and elitist MOEA in order to approximate the Pareto-optim...
Controlling high quality manufacturing processes: a robustness study of the lower-sided tbe ewma procedure
Pehlivan, Canan; Köksal, Gülser; Department of Industrial Engineering (2008)
In quality control applications, Time-Between-Events (TBE) type observations may be monitored by using Exponentially Weighted Moving Average (EWMA) control charts. A widely accepted model for the TBE processes is the exponential distribution, and hence TBE EWMA charts are designed under this assumption. Nevertheless, practical applications do not always conform to the theory and it is common that the observations do not fit the exponential model. Therefore, control charts that are robust to departures from ...
Adaptive Harmony Search Method for Structural Optimization
Hasançebi, Oğuzhan; Saka, Mehmet Polat (American Society of Civil Engineers (ASCE), 2010-04-01)
This paper presents an adaptive harmony search algorithm for solving structural optimization problems. The harmony memory considering rate and pitch adjusting rate are conceived as the two main parameters of the technique for generating new solution vectors. In the standard implementation of the technique appropriate constant values are assigned to these parameters following a sensitivity analysis for each problem considered. The success of the optimization process is directly related on a chosen parameter ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
İ. Karahan, “Preference-based flexible multiobjective evolutionary algorithms,” M.S. - Master of Science, Middle East Technical University, 2008.
