Hybrid simplex-genetic algorithm for global numerical optimization
Guiqiang Chen, Zushu Li, Linjian Tang, Qing Liu · 2008
A hybrid simplex-genetic algorithm (HSGA) is presented to solve global numerical optimization problems. The HSGA combines the traditional genetic algorithm, which has a powerful global exploration capacity, with simplex algorithm, which can exploit the local range. Some improved mechanism are introduced in the HSGA, such as hybrid encoding, orthogonal design, and feedback mutation etc. so the HSGA can be more robust, statically sound, and quickly convergent. The proposed HSGA is applied to solve benchmark problems. The computational experiments show that the HSGA can find the optimal or close-to-optimal solutions. It is also validated that the HSGA is efficient.