Abstract
The differential evolution algorithm is robust, easy to use, requires few control parameters, and has various optimization strategies. Based on analysis of advantages and disadvantages of these optimization strategies, a modified differential evolution algorithm with hybrid optimization strategy is proposed. The main idea of the modified differential evolution algorithm is to divide all of the individuals into two groups randomly, and the two groups adopt different optimization strategies. The convergence speed and search succeed probability of the modified differential evolution are tested using five benchmark functions for optimization algorithm, and the results are compared with dynamic differential evolution and particle swarm optimization. From the simulation results, it is observed that the search efficiency of the modified differential evolution is significantly improved as well as the high search succeed probability is ensured.
| Original language | English |
|---|---|
| Pages (from-to) | 2402-2405 |
| Number of pages | 4 |
| Journal | Tien Tzu Hsueh Pao/Acta Electronica Sinica |
| Volume | 34 |
| Issue number | SUPPL. |
| State | Published - Dec 2006 |
Keywords
- Differential evolution algorithm
- Optimization algorithm
- Optimization strategy
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