A novel lower-dimensional-search algorithm for numerical optimization

  • Authors:

  • Hui Shi;Sanyou Zeng;Hui Wang;Gang Liu;Guang Chen;Hugo de Garis;Lishan Kang

  • Affiliations:

  • School of Computer Science, China University of Geosciences, Wuhan, China and Research Center of Science and Technology, China University of Geosciences, Wuhan, China;School of Computer Science, China University of Geosciences, Wuhan, China and Research Center of Science and Technology, China University of Geosciences, Wuhan, China;School of Computer Science, China University of Geosciences, Wuhan, China and Research Center of Science and Technology, China University of Geosciences, Wuhan, China;School of Computer Science, China University of Geosciences, Wuhan, China and Research Center of Science and Technology, China University of Geosciences, Wuhan, China;School of Computer Science, China University of Geosciences, Wuhan, China and Research Center of Science and Technology, China University of Geosciences, Wuhan, China;School of Computer Science, China University of Geosciences, Wuhan, China and International School of Software, Wuhan University, Wuhan, China;School of Computer Science, China University of Geosciences, Wuhan, China and StateKey Laboratory of Software Engineering, Wuhan University, Wuhan, China

  • Venue:
  • ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
  • Year:
  • 2007

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Abstract

In the present study, a novel strategy of Lower-dimensional-Search Algorithm (LDSA) is proposed for solving the complex numerical optimization problems. The crossover operator of the LDSA algorithm searches a lower-dimensional neighbor of the parent points where the neighbor center is the parents' barycenter, therefore, the new algorithm converges fast. The niche impaction operator and the offspring mutation operator preserve the diversity of the population. The proposed LDSA strategies are applied to 22 test problems. These functions are widely used as benchmark in numerical optimization. The experimental results are reported here show that the LDSA algorithm is an effective algorithm for the complex numerical optimization problems. What's more is that the LDSA algorithm is simple and easy to be implemented.