Statistics and Computing

, Volume 16, Issue 3, pp 251–260 | Cite as

Comparative analysis of modern optimization tools for the p-median problem

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Abstract

This paper develops a study on different modern optimization techniques to solve the p-median problem. We analyze the behavior of a class of evolutionary algorithm (EA) known as cellular EA (cEA), and compare it against a tailored neural network model and against a canonical genetic algorithm for optimization of the p-median problem. We also compare against existing approaches including variable neighborhood search and parallel scatter search, and show their relative performances on a large set of problem instances. Our conclusions state the advantages of using a cEA: wide applicability, low implementation effort and high accuracy. In addition, the neural network model shows up as being the more accurate tool at the price of a narrow applicability and larger customization effort.

Keywords

Evolutionary algorithms Cellular genetic algorithms Neural networks Optimization tools p-median 

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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.University of MálagaSpain

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