Identifying preferred solutions in multiobjective combinatorial optimization problems

Date

2019-01-01

Author

Lokman, Banu

Metadata

Show full item record

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

Item Usage Stats

281
views

0
downloads

We develop an evolutionary algorithm for multiobjective combinatorial optimization problems. The algorithm aims at converging the preferred solutions of a decision-maker. We test the performance of the algorithm on the multiobjective knapsack and multiobjective spanning tree problems. We generate the true nondominated solutions using an exact algorithm and compare the results with those of the evolutionary algorithm. We observe that the evolutionary algorithm works well in approximating the solutions in the preferred regions.

Subject Keywords

Evolutionary Algorithm, Preferred Region, Nondominated Frontier, Multiobjective Combinatorial Optimization

URI

https://hdl.handle.net/11511/32559

Journal

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES

DOI

https://doi.org/10.3906/elk-1807-18

Collections

Graduate School of Natural and Applied Sciences, Article

Suggestions

OpenMETU
Core

An interactive preference based multiobjective evolutionary algorithm for the clustering problem Demirtaş, Kerem; Özdemirel, Nur Evin; Karasakal, Esra; Department of Industrial Engineering (2011) We propose an interactive preference-based evolutionary algorithm for the clustering problem. The problem is highly combinatorial and referred to as NP-Hard in the literature. The goal of the problem is putting similar items in the same cluster and dissimilar items into different clusters according to a certain similarity measure, while maintaining some internal objectives such as compactness, connectivity or spatial separation. However, using one of these objectives is often not sufficient to detect differ...
An interactive evolutionary metaheuristic for multiobjective combinatorial optimization Phelps, S; Köksalan, Mustafa Murat (2003-12-01) We propose an evolutionary metaheuristic for multiobjective combinatorial optimization problems that interacts with the decision maker (DM) to guide the search effort toward his or her preferred solutions. Solutions are presented to the DM, whose pairwise comparisons are then used to estimate the desirability or fitness of newly generated solutions. The evolutionary algorithm comprising the skeleton of the metaheuristic makes use of selection strategies specifically designed to address the multiobjective na...
Finding all nondominated points of multi-objective integer programs LOKMAN, BANU; Köksalan, Mustafa Murat (2013-10-01) We develop exact algorithms for multi-objective integer programming (MIP) problems. The algorithms iteratively generate nondominated points and exclude the regions that are dominated by the previously-generated nondominated points. One algorithm generates new points by solving models with additional binary variables and constraints. The other algorithm employs a search procedure and solves a number of models to find the next point avoiding any additional binary variables. Both algorithms guarantee to find a...
An interactive approach for biobjective integer programs under quasiconvex preference functions Ozturk, Diclehan Tezcaner; Köksalan, Mustafa Murat (2016-09-01) We develop an interactive algorithm for biobjective integer programs that finds the most preferred solution of a decision maker whose preferences are consistent with a quasiconvex preference function to be minimized. During the algorithm, preference information is elicited from the decision maker. Based on this preference information and the properties of the underlying quasiconvex preference function, the algorithm reduces the search region and converges to the most preferred solution progressively. Findin...
An Efficient Metaheuristic Algorithm for Engineering Optimization: SOPT Hasançebi, Oğuzhan (2012-06-01) Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems so far. In the present study, a simple optimization (SOPT) algorithm with two main steps; namely exploration and exploitation, is provided for practical applications. Aside from a reasonable rate of convergence attained, the ease in its implementation and dependency on few parameters only are among the advantageous characteristics of the proposed SOPT algorithm. The efficienc...

Citation Formats

B. Lokman, “Identifying preferred solutions in multiobjective combinatorial optimization problems,” TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, pp. 1970–1981, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32559.