Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization
Applied and Computational Mathematics
Volume 4, Issue 2-1, April 2015, Pages: 1-6
Received: Jan. 21, 2015; Accepted: Jan. 22, 2015; Published: Mar. 2, 2015
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Authors
Hadi Akbarzade Khorshidi, School of Applied Science and Engineering, Faculty of Science, Monash University, Melbourne, Australia
Sanaz Nikfalazar, Faculty of Management, Department of Public Administration, University of Tehran, Tehran, Iran
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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.
Keywords
Reliability-Redundancy Allocation Problem (RRAP), Imperialist Competitive Algorithm (ICA), Genetic Algorithm (GA), System Reliability Optimization (SRO), Multi-State Weighted k-out-of-n Systems
To cite this article
Hadi Akbarzade Khorshidi, Sanaz Nikfalazar, Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization, Applied and Computational Mathematics. Special Issue: Quality, Reliability, Safety, and Risk Modeling and Optimization. Vol. 4, No. 2-1, 2015, pp. 1-6. doi: 10.11648/j.acm.s.2015040201.11
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