A Novel Cuckoo Search Optimization Algorithm Base on Gauss Distribution

This paper according to the low convergence of rate of Cuckoo Search (CS) algorithm, a novel cuckoo search optimization algorithm base on Gauss distribution (GCS) is presented. We then apply the GCS algorithm to solve standard test functions and engineering design optimization problems, the optimal solutions obtained by GCS are far better than the best solutions obtained by CS, and the GCS has a high convergence rate.

[1]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[2]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[3]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[4]  Leon S. Lasdon,et al.  Optimization in engineering design , 1967 .

[5]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[8]  Xin-She Yang,et al.  Biology-Derived Algorithms in Engineering Optimization , 2010, Handbook of Bioinspired Algorithms and Applications.

[9]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[10]  Zhang Mei-feng,et al.  Hybrid Artificial Fish Swarm Optimization Algorithm Based on Mutation Operator and Simulated Annealing , 2006 .