Triangle Evolution--A Hybrid Heuristic for Global Optimization
Received:March 19, 2007  Revised:March 24, 2007
Key Word: global optimization   evolutionary computation   differential evolution   simplex method.  
Fund ProjectL:the National Natural Science Foundation of China (No.10671029).
Author NameAffiliation
LUO Chang Tong Institute of Mathematics, Jilin University, Jilin 130012, China
Jilin Institute of Architecture and Civil Engineering, Jilin 130021, China 
YU Bo Department of Applied Mathematics, Dalian University of Technology, Liaoning 116024, China 
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      This paper presents a hybrid heuristic--triangle evolution (TE) for global optimization. It is a real coded evolutionary algorithm. As in differential evolution (DE), TE targets each individual in current population and attempts to replace it by a new better individual. However, the way of generating new individuals is different. TE generates new individuals in a Nelder-Mead way, while the simplices used in TE is 1 or 2 dimensional. The proposed algorithm is very easy to use and efficient for global optimization problems with continuous variables. Moreover, it requires only one (explicit) control parameter. Numerical results show that the new algorithm is comparable with DE for low dimensional problems but it outperforms DE for high dimensional problems.
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