Immune Algorithm Versus Differential Evolution: A Comparative Case Study Using High Dimensional Function Optimization

  • Authors:

  • Vincenzo Cutello;Natalio Krasnogor;Giuseppe Nicosia;Mario Pavone

  • Affiliations:

  • Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125 Catania, Italy;Automated Scheduling, Optimisation and Planning Research Group, School of Computer Sciences and IT, Jubilee Campus, University of Nottingham, Nottingham, NG81BB, UK;Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125 Catania, Italy;Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125 Catania, Italy

  • Venue:
  • ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
  • Year:
  • 2007

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Abstract

In this paper we propose an immune algorithm (IA) to solve high dimensional global optimization problems. To evaluate the effectiveness and quality of the IAwe performed a large set of unconstrained numerical optimisation experiments, which is a crucial component of many real-world problem-solving settings. We extensively compare the IA against several Differential Evolution (DE) algorithms as these have been shown to perform better than many other Evolutionary Algorithms on similar problems. The DE algorithms were implemented using a range of recombination and mutation operators combinations. The algorithms were tested on 13 well known benchmark problems. Our results show that the proposed IA is effective, in terms of accuracy, and capable of solving large-scale instances of our benchmarks. We also show that the IA is comparable, and often outperforms, all the DE variants, including two Memetic algorithms.