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Ant Colony Optimization Algorithm with Three Types of Pheromones for the Component Assignment Problem in Linear Consecutive-k-out-of-n:F Systems

In: Predictive Analytics in System Reliability

Author

Listed:
  • Taishin Nakamura

    (Tokai University)

  • Isshin Homma

    (Tokyo Metropolitan University)

  • Hisashi Yamamoto

    (Tokyo Metropolitan University)

Abstract

The ant colony optimization (ACO) algorithm is a meta-heuristic optimization method used to solve challenging optimization problems. Notably, the pheromone model of ACO impacts algorithmic performance. Hence, this paper presents an ACO algorithm with three types of pheromones for solving the component assignment problem of the linear consecutive-k-out-of-n:F system. This configuration can be used to represent a real system in which consecutive failed components cause system failures. Moreover, the component assignment problem seeks a component arrangement in which system reliability is maximized. The proposed algorithm is incorporated with either adjacence-, position-, or k-interval-wise pheromones that are compared using a numerical experiment. The results indicate that the ACO algorithm with the position-wise pheromone performs well within the scope of the experiment.

Suggested Citation

  • Taishin Nakamura & Isshin Homma & Hisashi Yamamoto, 2023. "Ant Colony Optimization Algorithm with Three Types of Pheromones for the Component Assignment Problem in Linear Consecutive-k-out-of-n:F Systems," Springer Series in Reliability Engineering, in: Vijay Kumar & Hoang Pham (ed.), Predictive Analytics in System Reliability, pages 81-96, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-05347-4_6
    DOI: 10.1007/978-3-031-05347-4_6
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