An improved clone selection optimization algorithm based on prior knowledge

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Abstract

Though clone selection algorithm has been used successfully in many instances of optimizations, there is still difficultness when solving much complicated problems. Using prior knowledge of problems themselves leads a feasible approach. In this paper, two operators, named clonal adjust operator and immunodominance operator are designed based on clonal mechanisms and prior knowledge. With these, an improved clone selection algorithm is put forward to solve NP-hard combinatorial optimization. The simulations show that when applied to 0-1 knapsack benchmark data, the algorithm is effective and that achieves better results with quicker convergence than evolutionary algorithm.

Original languageEnglish
Title of host publicationProceedings - International Conference on Computer Science and Software Engineering, CSSE 2008
Pages374-377
Number of pages4
DOIs
StatePublished - 2008
EventInternational Conference on Computer Science and Software Engineering, CSSE 2008 - Wuhan, Hubei, China
Duration: 12 Dec 200814 Dec 2008

Publication series

NameProceedings - International Conference on Computer Science and Software Engineering, CSSE 2008
Volume1

Conference

ConferenceInternational Conference on Computer Science and Software Engineering, CSSE 2008
Country/TerritoryChina
CityWuhan, Hubei
Period12/12/0814/12/08

Keywords

  • Artificial immune system
  • Clonal selection
  • Knapsack problem
  • Optimization
  • Prior knowledge

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