MOBAIS: A Bayesian Artificial Immune System for Multi-Objective Optimization

  • Pablo A. D. Castro
  • Fernando J. Von Zuben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5132)


Significant progress has been made in theory and design of artificial immune systems (AISs) for solving multi-objective problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AIS to deal effectively with building blocks (high-quality partial solutions coded in the antibody). The available AISs present mechanisms for evolving the population that do not take into account the relationship among the variables of the problem, causing the disruption of these high-quality partial solutions. Recently, we proposed a novel immune-inspired approach for single-objective optimization as an attempt to avoid this drawback. Our proposal replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network representing the joint distribution of promising solutions and, subsequently, uses this model for sampling new solutions. Now, in this paper we extend our methodology for solving multi-objective optimization problems. The proposal, called Multi-Objective Bayesian Artificial Immune System (MOBAIS), was evaluated in the well-known multi-objective Knapsack problem and its performance compares favorably with that produced by contenders such as NSGA-II, MISA, and mBOA.


Bayesian Network Pareto Front Multiobjective Optimization Knapsack Problem Multiobjective Optimization Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Pablo A. D. Castro
    • 1
  • Fernando J. Von Zuben
    • 1
  1. 1.Laboratory of Bioinformatics and Bioinspired Computing - LBiC Department of Computer Engineering and Industrial Automation - DCA School of Electrical and Computer Engineering - FEECUniversity of Campinas - UNICAMPCampinasBrazil

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