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基于蚁群遗传算法的电梯群控多目标优化的研究

论文编号:lw201704171136519106 所属栏目:电气工程论文 发布日期:2018年02月13日 论文作者:无忧论文网
摘 要


随着现在社会的进步,人们的对生活品质问题也有越来越高的要求,作为电梯服务,一部电梯现在已经不能满足人们的需求了,由此又出现了多个电梯同时使用,也就形成了电梯控制体系。控制体系有很多优势,包括有效疏通运输客流量,降低阻塞系数,也就因此受到了极大的关注,遗憾的是在中国国内的电梯种类和控制体系大多数都是国外进口的。所以我国想要开发研究电梯以及电梯控制体系就必须大力发展相关产业并积极创新。
控制计算是电梯控制体系的中心,国内许多电梯专业者都对于这种计算有着一定的研究并提出相应的解决措施,当然有好有坏。在该论文中是把蚁群遗传算法与电梯计算融合在一起进行研究。蚁群遗传算法是这些年才开发研究出的最新计算方法,计算过程是通过计算相应组件进行组解来获取最佳答案。计算技巧是通过将正回馈和负回馈相结合再进行优质计算得出准确答案,并且同时保持在正确数值范围内,这种计算方法是很符合那种有很多目标、不稳定性等问题的电梯控制项目上。
因为这种计算方法的解决目标主要是利用看图进行计算。在该论文中我们研发出一种结合呼唤讯号和电梯组合的模块。对电梯控制体系问题进行非具体性研究,把电梯相关问题进行调转变换形成最佳符合模型。电梯控制体系最主要是要进行配置优化更新问题,在该论文中最主要的优化问题是使用者的乘坐时间和等待时间,空间设置和体系进行运作能量耗损问题。把想进行的优化措施进行整合成一个数值,把它作为具体数值进行操作。利用蚁群遗传算法手法进行优化,获取最棒的计算结果,形成最佳计算方案。本文开展电梯群控系统中的算法研究。在电梯的群控调度算法上引入了遗传算法,利用遗传算法对客流交通模式及派梯规则进行优化,实现电梯调度规则的进化,以适应环境的变化。
该论文在Matlab环境下进行控制体系的各个选项的拟真操作。把进行拟真得出的结论和另外几个电梯体系计算方法进行了简单的质量比对,通过比对,我们得知了蚁群遗传算法手法是具有很大的优势。具体进行改善蚁群遗传算法方法在调整电梯性能方面问题的遗留问题,并通过对这些遗留问题进行整合,提出具体的解决方案:利用动态相加的方案寻找最佳相加机会进行整合;利用预测手法对蚁群遗传算法方法的大小值进行检测。在进行这些更改之后,可以把遗传计算方法和蚁群遗传算法方法有效的融合在一起,发挥各自的长处,更好的为电梯的相关性能进行调整解决。
该论文不但把蚁群遗传算法方法放在电梯性能问题上进行研究,更是为以后的多方面应用打下了良好的基础。对该论文中提出的多方向调研的整合方法和模板思想,是可以将蚁群遗传算法方法的使用区域进行放大,例如经济调配情况、课表排列情况和水电等具体应用情况等多方向调研的情况上。
关键词:电梯群控;蚁群算法;遗传算法;二部图;多目标优化 
 
ABSTRACT


With the prevalence of intelligent buildings, elevator has become more and moreimportant as the rilain conveyance between floors. More attention is paid to the highperformance of elevator system. In this paper, the reseaich on the intelligent algorithm for elevator group control is presented. The multilayer framework ofcommunication and the reliable control strategies are adopted in this novel system,which is based on the embedded microprocessor. The main content of the iesearchare as the followings.
Group control algorithms of elevator group control system is the core, although experts and scholars at home and abroad on this issue have proposed a variety of solutions, but they all have their advantages and disadvantages. This combination of the Ant Colony algorithm and elevator group control problems. Ant Colony algorithm is more than 10 years to bring a new kind of evolutionary algorithms, through the Group of candidate solutions evolved to seek optimal solutions. Through positive feedback and negative feedback mechanisms to enable algorithm in the optimal direction and keep the search scope to avoid stagnation, Ant Colony algorithm suitable for use in a multi-objective elevator group control, nonlinear and uncertain issues.
Due to the application of Ant Colony algorithm to solve the problem, need to be able to use the graph to describe. Based on research in this paper call signal and the bipartite model of elevator group. Abstract the problem of elevator group control will solve the problem of elevator group control to find a maximum matching in bipartite graphs. Elevator group control problems which are multi-objective optimization problem, we set the target waiting time for passengers, riding time, congestion and energy consumption of elevator system. Through a combination of weighted combinations for a function, the function set as weighted for bipartite graph edge set. By Ant Colony algorithm for bipartite graphs best match search, using Ant Colony algorithm generates optimal dispatch plan.
Group control system is based on the simulation under Matlab environment all aspects of elevator group control algorithm based on Ant Colony algorithm for simulation. Simulation results and other adjustable ladder algorithm performance comparison, verify the application of Ant Colony algorithm in elevator group control problems (especially when the traffic-intensive) superiority. Focuses on hybrid algorithm of Ant Colony Optimization, genetic and traditional enough, to solve these problems made a number of improvements: dynamic integration policy was introduced to ensure the best fusion of two time; the introduction of grey prediction model to estimate the maximum and minimum pheromone of Ant Colony algorithm and bound. Through these improvements, making the dynamics of genetic algorithm and Ant Colony algorit