New operators for multi‐objective clonal selection algorithms

• Lucas de S. Batista ^[1] ; Jaime A. Ramírez ^[1] ; Frederico G. Guimaraes ^[2]
  1. [1] Universidade Federal de Minas Gerais

     Universidade Federal de Minas Gerais

     Brasil

     
  2. [2] Universidade Federal de Ouro Preto

     Universidade Federal de Ouro Preto

     Brasil

     
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 28, Nº 4 (Special Issue: Selected papers from 13th IGTE Symposium), 2009, págs. 833-850
• Idioma: inglés
• Enlaces
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• Resumen

  • Purpose – The purpose of this paper is to present a new multi‐objective clonal selection algorithm (MCSA) for the solution of electromagnetic optimization problems.

    Design/methodology/approach – The method performs the somatic hypermutation step using different probability distributions, balancing the local search in the algorithm. Furthermore, it includes a receptor editing operator that implicitly realizes a dynamic search over the landscape.

    Findings – In order to illustrate the efficiency of MCSA, its performance is compared with the nondominated sorting genetic algorithm II (NSGA‐II) in some analytical problems and in the well‐known TEAM benchmark Problem 22. Three performance evaluation techniques are used in the comparison, and the effect of each operator of the MCSA in its accomplishment is estimated.

    Research limitations/implications – In the analytical problems, the MCSA enhanced both the extension and uniformity in its solutions, providing better Pareto‐optimal sets than the NSGA‐II. In the Problem 22, the MCSA also outperformed the NSGA‐II. The MCSA was not dominated by the NSGA‐II in the three variables case and clearly presented a better convergence speed in the eight variables problem.

    Practical implications – This paper could be useful for researchers who deal with multi‐objective optimization problems involving high‐computational cost.

    Originality/value – The new operators incorporated in the MCSA improved both the extension, uniformity and the convergence speed of the solutions, in terms of the number of function evaluations, representing a robust tool for real‐world optimization problems.