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

Taishin Nakamura (), Isshin Homma () and Hisashi Yamamoto ()
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Taishin Nakamura: Tokai University
Isshin Homma: Tokyo Metropolitan University
Hisashi Yamamoto: Tokyo Metropolitan University

A chapter in Predictive Analytics in System Reliability, 2023, pp 81-96 from Springer

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-05347-4_6

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DOI: 10.1007/978-3-031-05347-4_6

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