Journal of Heuristics

, Volume 14, Issue 4, pp 313–333

Pareto memetic algorithm for multiple objective optimization with an industrial application

  • Arnaud Zinflou
  • Caroline Gagné
  • Marc Gravel
  • Wilson L. Price


Multiple objective combinatorial optimization problems are difficult to solve and often, exact algorithms are unable to produce optimal solutions. The development of multiple objective heuristics was inspired by the need to quickly produce acceptable solutions. In this paper, we present a new multiple objective Pareto memetic algorithm called PMSMO. The PMSMO algorithm incorporates an enhanced fine-grained fitness assignment, a double level archiving process and a local search procedure to improve performance. The performance of PMSMO is benchmarked against state-of-the-art algorithms using 0–1 multi-dimensional multiple objective knapsack problem from the literature and an industrial scheduling problem from the aluminum industry.


Memetic algorithms Multiple objectives Combinatorial optimization Niche Elitism 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Arnaud Zinflou
    • 1
  • Caroline Gagné
    • 1
  • Marc Gravel
    • 1
  • Wilson L. Price
    • 2
  1. 1.Département d’Informatique et de MathématiqueUniversité du Québec à ChicoutimiChicoutimiCanada
  2. 2.Faculté des Sciences de l’AdministrationUniversité LavalSte-FoyCanada

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