Water Quality Parameters Identification Model Based on Artificial Fish Swarm Algorithm with Adaptive Parameter Optimization
نویسندگان
چکیده
In view of the bad convergence performance and low precision of standard artificial fish swarm algorithm in the water quality properties identification, this paper put forward an improved identification model based on adaptive parameters optimization. Firstly, it optimized the immune cloning and selection algorithm (ICSA) in periodic mutation operator and selection operator. Then it introduced the diversity and immune memory property of the optimized immune cloning and selection algorithm into the artificial fish swarm algorithm and optimized the parameters adaptively. The diversity maintaining strategy based on concentration mechanism was adopted to keep a certain of artificial fish concentration of different fitness values in the new generation of artificial fish swarm. Finally, the improved artificial fish swarm algorithm was used in the identification of water quality parameters. The simulation experiments show that the improved artificial fish swarm algorithm in this paper has better convergence performance than traditional ones and has lower error in the water quality property identification.
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