Research Of Artificial Fish Swarm Algorithm Improvement

The Artificial Fish Swarm Algorithm is a new stochastic optimization algorithm based on group intelligent, essentially is a complex intelligent system. It has a stronger robustness, the fine distributed computing machine-made, easy to union with other methods, and so on. At present to this algorithm research, the application already seeped to many applications domain, and developed from the solution in unidimensional static state optimization questions to the solution in multi-dimensional dynamic combination optimization questions. The Artificial Fish Swarm Algorithm has already become in the interdisciplinary studies as an extremely active front research question.First, this paper briefly discusses the basic idea of AFSA, characteristics and the research present situation; elaborate to it conducts the improvement research significance. Then discussed several kinds of method to make the improvement to the algorithm, which includes: Grid-based strategy to improve the algorithm, the EAFSA algorithm which adds escapability to artificial fish, the PAFSA algorithm with propagation and the ability to join a number of artificial fishing operator algorithm. In based on in the grid division strategy algorithm improvement, uses grid division strategy to the continual territory optimized question separate, enhanced the algorithm to the exact solution gain ability, simultaneously the taboo search thinking introduction also speeds up the algorithm's speed; In the improved EAFSA algorithm, we carry on the population division to the artificial fish swarm. While the ordinary behavior population's artificial fish normally seeks superior, joins escapable behavior artificial fish swarm to carry on the overall situation optimal solution territory seeking, is helpful jumps out the partial optimal solution territory to the guidance ordinary fish swarm, strengthened the algorithm's astringency; In the improved PAFSA algorithm, we carry on to the artificial fish group based on the similar rank division, the different rank artificial fish in the algorithm iterative process function is different: Either carries on the overall situation to search the region mining, either carries on the partial optimal solution excavating, this has realized the search efficiency and the effect balance; In the artificial fish's behavior improvement, introduced the multi-father body hybrid operator, the auto-adapted delta variation operator, the double arithmetic overlapping operator, the peak-jumping operator and so on, has carried on the optimization to the artificial fish's individual behavior, greatly strengthened the algorithm's ability to solve the highly complex questions. Finally simply summarized to the Artificial Fish Swarm Algorithm improvement effect and the next research direction.The present paper has conducted the thorough research and the attempt to the Artificial Fish Swarm Algorithm improvement method, after the improvement, AFSA has the higher search efficiency and the gain optimal solution ability. The present paper research results have the important reference significance regarding the application of AFSA to solution actual optimization question. Further the thorough research also has the higher reference value to the next research on AFSA.