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Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

  • Wang, Xinqing1
  • Zhao, Yang1
  • Wang, Dong1
  • Zhu, Huijie1
  • Zhang, Qing2
  • 1 University of Science and Technology of Chinese People’s Liberation Army, Nanjing, 210007, China , Nanjing (China)
  • 2 Tianjin University, School of Mechanical Engineering, Tianjin, 300072, China , Tianjin (China)
Published Article
Chinese Journal of Mechanical Engineering
Springer Berlin Heidelberg
Publication Date
Sep 28, 2013
DOI: 10.3901/CJME.2013.05.1031
Springer Nature


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.

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