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Goal in this study are: 1) The overall pattern of mussels movement has carried on the further formal description and discussion;2) give mussels movement patterns and river landscape features;3) the overall pattern of swarm intelligence algorithms for pseudo equation and block diagram, and a unified framework of swarm intelligence computation pattern of a concept, the overall hierarchical framework model of swarm intelligence are given.In view of this, this paper studies the movement of mussels model and river landscape characteristics, gives the expression of random motion pattern in;Learning mussels group of the forming process of river pattern, and on this basis to random movement based on the MATLAB simulation model of mussels group of river bed.
Paper main research content includes:
(1) Artificial intelligence summary.
(2) The mussels landscape pattern formation, the swarm intelligence algorithm of optimal dynamic performance evaluation model research, under the guidance of basic index system of intelligent optimization, particle swarm optimization function optimization problems, for example, ensi to build a compreh ve evaluation algorithm to the overall optimization
performance and overall optimization of dynamic group dynamic effectiveness evaluation model system, particle swarm optimization algorithm and the optimal value of the dynamic degree of polymerization, group dynamics, center of gravity of convergence degree dynamic colony persity and dynamic evaluation model of the instance simulation and validation.
(3) The application of swarm intelligence in the field of study.First introduce the swarm intelligence theory to study of university ranking system, based on particle swarm algorithm study and optimization research system of university ranking index;Then will swarm intelligence has been applied in the field of semiconductor manufacturing development research, based on the actual production data, to a solder ball back in the typical process of incomplete primary and secondary causes of swarm intelligence analysis, and for a class of simplified assembly job shop scheduling optimization problem, puts forward a dynamic scheduling method based on swarm intelligence.
Finally, the important content in the research of the whole piece of paper has carried on the profound summary, and river pattern formation of mussels group of random motion simulation, the goal and direction of future research work.
Key word: Nature, artificial intelligence, intelligent optimization, Brownian motion, mussel beds, swarm intelligence, MWO model
目录
第1章 绪论 1
1.1引言 1
1.2人工智能的发展与应用 2
1.3本文研究内容与创新点 7
1.4章节安排 8
第2章自然界群体智能的前景和研究应用 9
2.1自然界群体智能 9
2.2自然界群体智能的研究分支 10
2.3自然界群体智能运动的统一模型 12
2.3.1布朗运动模型 12
2.3.2高斯随机运动模型 15
2.3.3 Levy walk运动模型 18
2.3.4 Rayleigh diffusion运动模型 18
2.4自然界群体智能映射模型与计算实验 19
2.4.1自然界群体智能映射模型 19
2.4.2计算实验 20
2.5本章小结 21
第3章 贻贝群随机运动 22
3.1群体智能算法 22
3.2贻贝群随机运动的河床模式 22
3.2.1贻贝河床模式的自然特性 22
3.2.2贻贝群随机运动的河床模式形成 24
3.3实验设计 25
3.3.1实验设置 25
3.3.2实验结果 27