An introduction to population approaches for optimization and hierarchical objective functions: A discussion on the role of tabu search
 Pablo Moscato
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Population approaches suitable for global combinatorial optimization are discussed in this paper. They are composed of a number of distinguishable individuals called "agents", each one using a particular optimization strategy. Periods of independent search follow phases on which the population is restarted from new configurations. Due to its intrinsic parallelism and the asynchronicity of the method, it is particularly suitable for parallel computers. Results on two test problems are presented in this paper. The individual search optimization strategies for each agent have been chosen having the basic characteristics of tabu search. This has been done in order to avoid mixing the hypothesized properties of these population approaches with those of more elaborate tabu search strategies, but remarking on its main characteristics. A set of four test problem "landscapes" is discussed and their use to improve and benchmark the results by using tabu search as the individual optimization strategy within a population heuristic is suggested and explored. The application of tabu search to new problem areas, like molecular biology, is also investigated.
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 Title
 An introduction to population approaches for optimization and hierarchical objective functions: A discussion on the role of tabu search
 Journal

Annals of Operations Research
Volume 41, Issue 2 , pp 85121
 Cover Date
 19930601
 DOI
 10.1007/BF02022564
 Print ISSN
 02545330
 Online ISSN
 15729338
 Publisher
 Baltzer Science Publishers, Baarn/Kluwer Academic Publishers
 Additional Links
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 Authors

 Pablo Moscato ^{(1)}
 Author Affiliations

 1. CeTAD, Universidad Nacional de La Plata, C.C. 75, 1900, La Plata, Argentina