Research On The Modified Artificial Fish Swarm Optimization Algorithm And Its Applications

In order to solve the optimization problems extensively existing in the industrial processes, intelligent optimization algorithms have been developed by simulating the certain nature and social processes since the 1980s, which provide new approaches to get the optimization of some complex systems. Intelligent optimization algorithms have attracted a lot of attentions from researchers around the world and have been applied in many areas. Artificial Fish Swarm Algorithm (AFSA) is a new kind of swarm intelligent bionic algorithm based on the "looking for food" behaviour of fish swarm. AFSA has been proved to have many advantages, such as insensitivity to the initial values and parameters varying, easy implementation, the abilities of parallel processing and global search, and so on. However, some shortcomings exist in AFSA, such as slower convergence speed, hard to local all optima for multimodal problem. So it is very significant to improve basic AFSA to solve concrete engineering problems. The main contents of the dissertation are as follows:(1) When using AFSA to search optimization in a larger and smoother region, the algorithm has the problems of slower speed of convergence to the global optimum and weaker search ability, especially near to the optimum. This paper proposes a hybrid artificial fish swarm optimization algorithm based on the mutation operator and the simulated annealing. The implementation of the hybrid algorithm is as simple as that of AFSA, and the algorithm can also overcome the limitations of artificial fish stochastic moving without a definite purpose or gathering around the local optimum solution. The operation efficiency and searching ability of the hybrid algorithm are greatly improved, which gives an effective method to solve the problem of complex searching optimization. The feasibility and effectiveness of the hybrid algorithm are verified by the test to function and practical problem.(2) It is difficult to find all of the optimum when AFSA is used in multimodal optimization, so a niche artificial fish swarm algorithm (NAFSA) based on basic AFSA is proposed. NAFSA combines the niche technique and the simulated annealing method with AFSA. Moreover, the ideas of mutation operator and automatic calculating the niche radius are used in NAFSA. NAFSA is applied to the optimizations of some typical multimodal functions. The experimental results show that NAFSA can locate all of the optimal solutions including the global ones and local ones effectively and accurately. Furthermore, NAFSA not only has the good performance, but also can realize self-adapting searching.(3) When mining continuous attributes classification rules, the discrete pre-process is needed, which will cause the decrease of the accuracy of original information data. A classification rules mining algorithm based on AFSA is proposed. To make it suitable to AFSA algorithm, a new classification rule coding is designed and a function is defined to evaluate the classification rule. The algorithm solves the classification problem from the perspective of optimization and implements the classification rules mining of continuous attribute samples automatically, which presents a new approach to mine continuous data directly. The simulation results show that the proposed algorithm can mine better classification rules, including rule sets with higher accuracy, stronger robustness, the smaller number of rules, and simpler rule with fewer terms.(4) Aiming at the problem of determining the neural network architecture by experience, a network classifier is proposed based on AFSA. The algorithm combines the selection of input attributes and design of network architecture. The choice of input attributes, network architecture and parameters optimization are realized by AFSA, simultaneously. The experimental results demonstrate that the algorithm can achieve a simpler classifier which has a more reliable performance and better generalization ability. The difficulty of determining neural network architecture by experience is overcome. The application areas of AFSA are also extended.(5) On the basis of the research and improvement of AFSA, some sample data are obtained from the experiments of the project on the biological hydrogen production technology with solar energy supported by national "863" plan, AFSA is employed to optimize the topology structure of neural network. The main parameters, which influence the hydrogen production quantity, are obtained. The process is modelled based on optimization neural network. Finally, AFSA is used to optimize the main process conditions of hydrogen production, which can ensure to obtain the optimal conditions in which the maximum hydrogen production quality can be obtained. The experimental results show that the proposed optimization computation project is feasible. The research provides a new way for the technology optimization of biological hydrogen production by solar energy.The dissertation is a series research supported by national "863" plan (No. 2004AA515010) and the National Natural Science Foundation (No. 50676029).