A Memetic Genetic Algorithm for the Vertex p-center Problem | Zendy

Wayne Pullan | Zendy

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A Memetic Genetic Algorithm for the Vertex p-center Problem

Author(s) -

Wayne Pullan

Publication year - 2008

Publication title -

evolutionary computation

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.732

H-Index - 82

eISSN - 1530-9304

pISSN - 1063-6560

DOI - 10.1162/evco.2008.16.3.417

Subject(s) - crossover , benchmark (surveying) , memetic algorithm , center (category theory) , vertex (graph theory) , heuristic , genetic algorithm , algorithm , set (abstract data type) , mathematical optimization , population , computer science , local search (optimization) , range (aeronautics) , mutation , mathematics , theoretical computer science , artificial intelligence , engineering , biology , genetics , aerospace engineering , graph , chemistry , geodesy , programming language , gene , crystallography , geography , sociology , demography

The p-center problem is one of choosing p facilities from a set of candidates to satisfy the demands of n clients in order to minimize the maximum cost between a client and the facility to which it is assigned. In this article, PBS, a population based meta-heuristic for the p-center problem, is described. PBS is a genetic algorithm based meta-heuristic that uses phenotype crossover and directed mutation operators to generate new starting points for a local search. For larger p-center instances, PBS is able to effectively utilize a number of computer processors. It is shown empirically that PBS has comparable performance to state-of-the-art exact and approximate algorithms for a range of p-center benchmark instances.

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