A Selective Teaching-Learning Based Niching Technique with Local Diversification Strategy

Real world problems present instances where more than one optimal solution can be obtained for a system under consideration so as to switch between them without considerably affecting efficiency. In such instances the idea of niching provides a solution. In this paper we propose a swarm-based niching technique that enhances diversity by Teaching and Learning strategy that adapts to the local neighbourhood by controlled exploitation and the knowledge learned helps to preserve population diversity. Our algorithm, imitates the local-explorative swarm behaviour to hover around local sites in groups, exploiting the peaks with high degree of accuracy, is called TLB-lDS (Teaching-Learning Based Optimization with Local Diversification Strategy), without using any niching parameter. TLB-lDS algorithm is compared against sophisticated niching algorithms tested on a set of standard numerical benchmarks.

[1]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[2]  Ofer M. Shir,et al.  Adaptive Niche Radii and Niche Shapes Approaches for Niching with the CMA-ES , 2010, Evolutionary Computation.

[3]  Xiaodong Li,et al.  Efficient differential evolution using speciation for multimodal function optimization , 2005, GECCO '05.

[4]  G. Hardin The competitive exclusion principle. , 1960, Science.

[5]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  René Thomsen,et al.  Multimodal optimization using crowding-based differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[8]  Jacek M. Zurada,et al.  Swarm and Evolutionary Computation , 2012, Lecture Notes in Computer Science.

[9]  Xiaodong Li,et al.  Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology , 2010, IEEE Transactions on Evolutionary Computation.

[10]  Samir W. Mahfoud Niching methods for genetic algorithms , 1996 .

[11]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[12]  Alain Pétrowski,et al.  A clearing procedure as a niching method for genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[13]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[14]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[15]  Jing J. Liang,et al.  Differential Evolution With Neighborhood Mutation for Multimodal Optimization , 2012, IEEE Transactions on Evolutionary Computation.

[16]  Ponnuthurai N. Suganthan,et al.  Real-parameter evolutionary multimodal optimization - A survey of the state-of-the-art , 2011, Swarm Evol. Comput..

[17]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .