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| 1 | Improved Multi-objective Ant Colony Optimization Algorithm and Its Application in Complex Reasoning显示文摘The problem of fault reasoning has aroused great concern in scientific and engineering fields.However,fault investigation and reasoning of complex system is not a simple reasoning decision-making problem.It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints.So far,little research has been carried out in this field.This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes.Three optimization objectives are considered simultaneously: maximum probability of average fault,maximum average importance,and minimum average complexity of test.Under the constraints of both known symptoms and the causal relationship among different components,a multi-objective optimization mathematical model is set up,taking minimizing cost of fault reasoning as the target function.Since the problem is non-deterministic polynomial-hard(NP-hard),a modified multi-objective ant colony algorithm is proposed,in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives.At last,a Pareto optimal set is acquired.Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set,through which the final fault causes can be identified according to decision-making demands,thus realize fault reasoning of the multi-constraint and multi-objective complex system.Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model,which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system. | WANG Xinqing ZHAO Yang WANG Dong ZHU Huijie ZHANG Qing | 2013 | Chinese Journal of Mechanical Engineering2013，26，5： | 3 |
| 2 | 基于蚁群算法的LED分拣路径优化显示文摘提出基于蚁群算法对LED芯片分拣路径进行优化。对分拣工作芯片块间移动策略进行分析,降低盘片变形。在此基础上采用蚁群算法,建立分拣路径的全连接无向图并对分拣时间、分拣路径模型进行建模和优化。对蚂蚁数量、蚁群算法的时间复杂度、信息素挥发因子等参数进行优化。试验表明,该方法与传统方法相比能够缩短分拣时间,提高效率,为LED芯片分拣路径规划提供了一种有效的方法。 | 石炜 孙梁 董占民 孙红三 王建国 | 2012 | 机械工程学报2012，48，15： | 2 |
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