Research Output
A Hybrid Meta-Heuristic for Multi-Objective Optimization: MOSATS
  Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of solutions, called the Pareto-optimal front. Thus, the goal of multi-objective strategies is to generate a set of non-dominated solutions as an approximation to this front. However, the majority of problems of this kind cannot be solved exactly because they have very large and highly complex search spaces. In recent years, meta-heuristics have become important tools for solving multi-objective problems encountered in industry as well as in the theoretical field. This paper presents a novel approach based on hybridizing Simulated Annealing and Tabu Search. Experiments on the Graph Partitioning Problem show that this new method is a better tool for approximating the efficient set than other strategies also based on these meta-heuristics.

  • Type:


  • Date:

    30 May 2006

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  • Publisher

    Springer Science + Business Media

  • DOI:


  • Cross Ref:


  • ISSN:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science


Baños, R., Gil, C., Paechter, B., & Ortega, J. (2007). A Hybrid Meta-Heuristic for Multi-Objective Optimization: MOSATS. Journal of Mathematical Modelling and Algorithms, 6(2), 213-230.



hybrid meta-heuristics, multi-objective optimization, simulated annealing, tabu search

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