碩士 / 國立雲林科技大學 / 工業工程與管理系 / 102 / Recently, all social circles has gradually put more and more concentration on the Nanotechnology. In many preparation way of nano preparation.The wet-type mechanical milling process is a popular technology to produce nano-particles because of its applicability to all classes of materials. Process parameters of the wet-type milling milling time, flow velocity of circulation system, rotation velocity of agitator shaft, solute-to-solvent weight ratio and filling ratio of grinding media. The required qualities of the wet-type milling process of making the nano-particles are that the mean of grain size and the variance of nano-particle grain size must be kept small.It is a critical problem to find the optimal process parameters that can meet the two quality criteria.
Artificial fish swarm algorithm(AFSA) is a relatively new swarm intelligence-based optimization algorithm. It simulates the fundamental behaviors of fish such as prey, follow, swarm, and random moving to search optimal solution. It has excellent global search ability. But it cannot further perform search of finer grit. Particle Swarm Optimization(PSO) is able to converge quickly and is good at local search.
In the proposed algorithm, AFSA is integrated with PSO. ASFA is applied during the early search process to do global search and PSO is used during the final convergence process to do local search. In addition, a new leaping behavior will be proposed to avoid falling in local optimal solution. The proposed new method will be tested by a mathematic function to verify its feasibility, and then applied in the multiple quality criteria process to find the optimal process parameters. It is expected that the proposed new method will improve the searching of the AFSA both in efficiency and quality.
| Identifer | oai:union.ndltd.org:TW/102YUNT0031021 |
| Date | January 2014 |
| Creators | Jia-Jing Liang, 梁嘉晉 |
| Contributors | Tung-Hsu Hou, 侯東旭 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 77 |
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