The Research Of Artificial Fish Swarm Algorithm And Its Application

Today, technology is coming to a stage of intersection, infiltration, and interaction with multi-subjects. More and more issues on complexity, non-linearity, and system have come to us. To deal with such complexity of system, conventional techniques have become incapable, and to seek an optimization algorithm, which adapt to large-scale parallel with intelligent characteristics, has been a primary research target of related subjects. The artificial fish swarm algorithm(AFSA), a new method based on animal behaviors and the typical application of behaviorism artificial intelligence, was proposed by an internal scholar in recent years. It has been an advanced interdisciplinary research aspect. However, the study of AFSA is in a preliminary phase, the optimization performance and efficiency have gone with some disadvantages, such as some infant ideas, unsubstantial theoretical backup, poor capability of the balance between exploration and exploitation, blindness in the searching afterward, low optimized precision, and slow computation. Therefore, to study and perfect AFSA will promote the research and application of swarm intelligent algorithm, enhance its theoretical foundation, solve the existing problems with it, improve the adjustability and optimization performance of solving optimized problems, and extend the application field. Meanwhile it will also provide a new solution to the issues of complexity, non-linearity, and system.Aiming at unsubstantial theoretical foundation of the AFSA and the existing problems with it, this dissertation has systematically studied on AFSA from the aspects of biological elements, improvement method, topology, convergence, parameters, simplified model and application, etc.. The main achievements are as follows:1. The method of AFSA was studied in-depth, and some improved algorithms were introduced.①An improved AFSA was presented by dynamically adjusting the vision and step of artificial fish, improving the behavior of prey.②With the theory of orthogonal crossover experiments, an AFSA based on neighborhood orthogonal crossover operator was put forward by introducing neighborhood orthogonal crossover operator into the basic AFSA.③An artificial fish swarm cooperative evolution algorithm was proposed based on the parallelism of multi-swarms.④A hybrid optimization algorithm of PSO and AFSA was combined particle swarm optimization algorithm with artificial fish swarm algorithm.⑤A multi-agent artificial fish swarm algorithm was proposed by introducing the multi-agent system to the artificial fish swarm algorithm, the validity of which was validated through experiments.2. On the basis of the study on the topology structures of the swarm of the AFSA, experiments, and analysis on the performances of some generic topology structures, the global edition artificial fish swarm algorithm and an Von Neuman neighborhood-based artificial fish swarm algorithm were proposed. In the global edition artificial fish swarm algorithm, the artificial fish neighboring central and extreme position were substituted for the central and global extreme position of the swarm, thereby the amount of computation was reduced and the computational speed was improved. In the Von Neuman neighborhood-based artificial fish swarm algorithm every artificial fish was local to a certain extent, and the individual only exchanged message with those artificial fish around it, and the information of the individual was utilized adequately in swarm, which led the swarm to be evolved to divers direction. So due to the global algorithm the population diversity was able to be kept effectively and the precocity restrained. The results of tests indicated that these two algorithms based on topology had better optimization performance.3. By applying the Markov'basic theory, the convergence of the artificial fish swarm algorithm was proved and the effect of the algorithmic parameters to the algorithm performances was analyzed. Through experiments, parameters selection was researched in details, and some guidelines were summarized, which provided the useful reference for the further study of the AFSA.4. As to the disadvantages of low speed of AFSA, the artificial fish's behaviors of prey, swarm, follow and its behaviors'options were analyzed and improved. With an evolution equation, a simplified artificial fish swarm algorithm model was proposed, which had faster convergent speed and better optimization performance.5. The artificial fish swarm algorithm was applied into water resource environment projects, refering to simulating formula between water level and water flux, determining the transverse diffusive coefficient of river, and evaluating water quality of Yellow River in Lanzhou section, which turned out that the AFSA is able to resolve most optimization matters and has comprehensive practical interest and well applied foreground. In a word, in the dissertation, the artificial fish swarm algorithm was researched in depth and all round, some effective improvement methods were proposed, the convergence of the algorithm was demonstrated, the parameters performances were analyzed, and the simplified model of the algorithm was introduced, and the applications of the algorithm were implemented. Finally, the work of this dissertation was summed up, and further research directions were indicated.