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omni-aiNet: An Immune-Inspired Approach for Omni Optimization

  • Guilherme P. Coelho
  • Fernando J. Von Zuben
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4163)

Abstract

This work presents omni-aiNet, an immune-inspired algorithm developed to solve single and multi-objective optimization problems, either with single and multi-global solutions. The search engine is capable of automatically adapting the exploration of the search space according to the intrinsic demand of the optimization problem. This proposal unites the concepts of omni-optimization, already proposed in the literature, with distinctive procedures associated with immune-inspired concepts. Due to the immune inspiration, the omni-aiNet presents a population capable of adjusting its size during the execution of the algorithm, according to a predefined suppression threshold, and a new grid mechanism to control the spread of solutions in the objective space. The omni-aiNet was applied to several optimization problems and the obtained results are presented and analyzed.

Keywords

Pareto Front Multiobjective Optimization Objective Space Multiobjective Optimization Problem Nondominated Solution 
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 2006

Authors and Affiliations

  • Guilherme P. Coelho
    • 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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