Chaos gray-coded genetic algorithm and its application for pollution source identifications in convection–diffusion equation
✍ Scribed by Xiaohua Yang; Zhifeng Yang; Xinan Yin; Jianqiang Li
- Publisher
- Elsevier Science
- Year
- 2008
- Tongue
- English
- Weight
- 947 KB
- Volume
- 13
- Category
- Article
- ISSN
- 1007-5704
No coin nor oath required. For personal study only.
✦ Synopsis
In order to reduce the computational amount and improve computational precision for nonlinear optimizations and pollution source identification in convection-diffusion equation, a new algorithm, chaos gray-coded genetic algorithm (CGGA) is proposed, in which initial population are generated by chaos mapping, and new chaos mutation and Hooke-Jeeves evolution operation are used. With the shrinking of searching range, CGGA gradually directs to an optimal result with the excellent individuals obtained by gray-coded genetic algorithm. Its convergence is analyzed. It is very efficient in maintaining the population diversity during the evolution process of gray-coded genetic algorithm. This new algorithm overcomes any Hamming-cliff phenomena existing in other encoding genetic algorithm. Its efficiency is verified by application of 20 nonlinear test functions of 1-20 variables compared with standard binary-coded genetic algorithm and improved genetic algorithm. The position and intensity of pollution source are well found by CGGA. Compared with Gray-coded hybrid-accelerated genetic algorithm and pure random search algorithm, CGGA has rapider convergent speed and higher calculation precision.