Abstract
AbstractThe 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.
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