【作者】 彭海朋；

【导师】 王向东；

【作者基本信息】 沈阳工业大学 ， 系统工程， 2006， 硕士

【摘要】 现有的受蚂蚁种群行为启发而产生的优化算法,大多都是基于随机搜索机制的非确定性的概率理论发展而来的。但是近年来生物学家Cole发现整个蚁群行为是一种周期行为,然而单个蚂蚁的行为却是混沌的。显然混沌现象用Dorigo依据概率理论建立的蚁群优化模型是无法解释的。单个蚂蚁的混沌行为与群体的自组织和捕食行为之间是一种什么关系,这一点目前并没有引起国际群智能理论研究者广泛的关注。 受蚂蚁的混沌行为和自组织行为的启发,本文将蚂蚁混沌动力学、群组织和优化机制进行有机的结合,首先提出了一个新的群智能优化模型,详细分析了这个模型的动力学行为,然后将其成功的应用到几个不同的领域,并且取得了很好的效果,从而形成了一个新的关于群智能优化和混沌优化算法的详细理论。论文主要研究内容如下: 首先,基于群智能理论和混沌理论,给出了一个新的算法模型,即混沌蚂蚁群优化算法模型,讨论了邻居间的信息交换,分析了模型的动力学行为,并成功地应用于函数优化问题,在相同条件下与粒子群和凯尔曼群的结果作了比较。同时讨论了算法与其他混沌优化算法、蚁群算法和粒子群算法的异同。 其次,将混沌蚂蚁群优化算法成功的应用于神经网络系统的训练和模糊系统的设计,并利用混沌蚂蚁群优化算法设计的模糊系统进行非线性动态系统辨识和非线性自适应控制。 再次,采用混沌蚂蚁群算法对PID控制器的参数进行整定,以误差积分型性能指标为目标函数、以设计参数的取值范围及最小增益相位裕度为约束条件建立了数学模型。 最后,采用混沌蚂蚁群算法对混沌系统进行参数估计,并以典型的Lorenz混沌系统为例进行了计算机模拟。 数值仿真表明,设计的混沌蚂蚁群优化算法是有效的可行的。更多还原

【Abstract】 Most of the existing ant-inspired optimization algorithms are based on the random mechanism of non-deterministic probability theory. However, it has been suggested by biologist Cole that an ant colony exhibits a periodic behavior, while single ant show chaotic activity patterns. The chaotic phenomenon can not be explained by the mathematical model which is built based on probability theory of Marco Dorigo et al.. And the problem of how the chaotic behavior of single ant relates to the self-organizing and foraging behaviors of the ant colony has received little attention.Inspired by the chaotic behavior of single ant and self-organizing behavior of ant colony, we integrate the chaotic dynamics of ant, swarm organization with optimization mechanism and construct a new mathematical model of swarm intelligence optimization. We analyzed the nonlinear dynamical behaviors of the developed model in detail. Now the model has been applied in some diverse fields and achieved perfect effectiveness. Thus a new theory has been formed about the swarm intelligence optimization and chaos optimization. The main work and contribution of the present thesis are as follows:Firstly, based on the theories of swarm intelligence and chaos, a new mathematical optimization algorithm model, which is called chaotic ant swarm(CAS), is given and the information exchange mechanism in an ant colony is discussed. The dynamical behavior of the model is analyzed and is applied to the problem of numerical optimization. Under the same conditions, the results of particle swarm and Kalman swarm are compared. At the same time, similarities and differences among the new model and other chaos optimization algorithms, ant colony optimization algorithm, particle swarm algorithm are analyzed.Secondly, we introduce the proposed chaotic ant swarm optimization method to solve the training problems of neural networks and the design problem of fuzzy system to match desiredinput-output data pairs. Then the corresponding fuzzy system based on CAS optimization algorithm is built and applied to the identification and adaptive control of the unknown nonlinear dynamical systems.Thirdly, the proposed CAS optimization algorithm is used for the parameters tuning of PID controller. For the constructed optimization model, the objective function is the performance index of error integrality and the constrained conditions are the range of the designing parameter, the least gain and phase margin.Lastly, through the construction of a suitable fitness function, the problem of parameter estimation of the chaotic system is converted to a problem of parameter optimization1 which1 could be solved via chaotic ant swarm algorithm. Chaotic ant swarm algorithm has the ability’of global search. A numerical simulation on the well-known Lorenz chaotic system is conducted.Numerical simulation results are provided to show the effectiveness and feasibility of the developed CAS algorithm.更多还原