Abstract
Using meta-heuristic approaches to solve reliability and redundancy allocation problems (RRAP) has become attractive for researchers in recent years. In this paper, an optimization model is presented to maximize system reliability and minimize system cost simultaneously for multi-state weighted k-out-of-n systems. The model tends to optimize system design and maintenance activities over functioning periods that provides a dynamic modeling. A recently developed meta-heuristic approach imperialist competitive algorithm (ICA) and genetic algorithm (GA) are used to solve the model. The computational results have been compared to find out which approach is more appropriate for solving complex system reliability optimization models. It is shown that GA can find the better solution while ICA is a faster approach. In addition, an investigation is done on different parameters of the ICA.
Highlights
Reliability optimization models have been developed to find optimal decisions for engineering systems
An optimization model for multi-state weighted k-out-of-n system reliability is solved by two meta-heuristic approaches
A brief introduction is provided for both approaches in the context
Summary
Reliability optimization models have been developed to find optimal decisions for engineering systems. A reliability-redundancy allocation problem (RRAP) is formulated to determine the best selection for components in system design and improvement actions simultaneously. Different approaches such as exact, heuristic and meta-heuristic have been constructed to solve system reliability optimization (SRO) models [1]. Two meta-heuristic approaches are used to solve a complex RRAP model for multi-state k-out-of-n systems. This model tries to find the optimal number and mixture of components in the system, and is to apply economic maintenance actions during functioning periods.
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