A Memetic Algorithm for the Multidimensional Assignment Problem

Authors:
Gregory Gutin;Daniel Karapetyan
Affiliations:
Royal Holloway, University of London, London, UK;Royal Holloway, University of London, London, UK
Venue:
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Year:
2009

Citing 2
Cited 2

Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
A Memetic Algorithm for the Multidimensional Assignment Problem

SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics

A Memetic Algorithm for the Multidimensional Assignment Problem

SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
A new approach to population sizing for memetic algorithms: A case study for the multidimensional assignment problem

Evolutionary Computation

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Abstract

The Multidimensional Assignment Problem (MAP or s -AP in the case of s dimensions) is an extension of the well-known assignment problem. The most studied case of MAP is 3-AP, though the problems with larger values of s have also a number of applications. In this paper we propose a memetic algorithm for MAP that is a combination of a genetic algorithm with a local search procedure. The main contribution of the paper is an idea of dynamically adjusted generation size, that yields an outstanding flexibility of the algorithm to perform well for both small and large fixed running times.