An interactive evolutionary metaheuristic for multiobjective combinatorial optimization

Phelps, S
Köksalan, Mustafa Murat
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 nature of the problem. Interactions With the DM are triggered by a probabilistic evaluation of estimated fitnesses, while memory structures with indifference thresholds restrict the presentation of solutions resembling those that have already been rejected. The algorithm has been tested on a number of random instances of the Multiobjective Knapsack Problem (MOKP) and the Multiobjective Spanning Tree Problem (MOST). Simulation results indicate that the algorithm requires only a small number of comparisons to be made for satisfactory solutions to be found.


An interactive genetic algorithm applied to the multiobjective knapsack problem
Pamuk, S; Köksalan, Mustafa Murat (2001-01-01)
Multiobjective combinatorial problems are commonly encountered in practice and would benefit from the development of metaheuristics where the search effort is interactively guided towards the solutions favored by the decision maker. The present study introduces such an Interactive Genetic Algorithm designed for a general multiobjective combinatorial framework and discusses its behavior in simulations on the Multiobjective Knapsack Problem. The evolution strategies being employed reflect the multiobjective n...
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 evolutionary metaheuristic for approximating preference-nondominated solutions
Koekalan, Murat; Phelps, Selcen (Pamuk) (2007-03-01)
We propose an evolutionary metaheuristic for approximating the preference-nondominated solutions of a decision maker in multiobjective combinatorial problems. The method starts out with some partial preference information provided by the decision maker, and utilizes an individualized fitness function to converge toward a representative set of solutions favored by the information at hand. The breadth of the set depends on the precision of the partial information available on the decision maker's preferences....
Identifying preferred solutions in multiobjective combinatorial optimization problems
Lokman, Banu (2019-01-01)
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...
An Interactive partitioning approach for multiobjective decision making under a general monotone utility function
Karasakal, Esra (2013-09-01)
We develop an interactive partitioning approach for solving the multiobjective decision making problem of a decision maker (DM) who has an implicit general monotone utility function. The approach reduces feasible solution space using the DM's preferences. Hypothetical solutions called partition ideals (PIs) that dominate portions of the efficient frontier are generated and those that are inferior to a feasible solution are used to eliminate the dominated regions. We investigate the issues in representation ...
Citation Formats
S. Phelps and M. M. Köksalan, “An interactive evolutionary metaheuristic for multiobjective combinatorial optimization,” MANAGEMENT SCIENCE, pp. 1726–1738, 2003, Accessed: 00, 2020. [Online]. Available: