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 an...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.展开更多
基金the National Natural Science Foundation of China （No. 10671029）.
文摘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.
