An efficient metaheuristic for multi-level reliability optimization problem in electronic systems of the ship
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초록
The redundancy allocation problem has usually considered only the component redundancy at the lowest-level for theenhancement of system reliability. A system can be functionally decomposed into system, module, and component levels. Modular redundancy can be more effective than component redundancy at the lowest-level because in modular systems,duplicating a module composed of several components can be easier, and requires less time and skill. We consider amulti-level redundancy allocation problem in which all cases of redundancy for system, module, and component levels areconsidered. A tabu search of memory-based mechanisms that balances intensification with diversification via the short-term andlong-term memory is proposed for its solution. To the best of our knowledge, this is the first attempt to use a tabu search forthis problem. Our tabu search algorithm is compared with the previous genetic algorithm for the problem on the new composedtest problems as well as the benchmark problems from the literature. Computational results show that the proposed methodoutstandingly outperforms the genetic algorithm for almost all test problems.
Abstract
The redundancy allocation problem has usually considered only the component redundancy at the lowest-level for theenhancement of system reliability. A system can be functionally decomposed into system, module, and component levels. Modular redundancy can be more effective than component redundancy at the lowest-level because in modular systems,duplicating a module composed of several components can be easier, and requires less time and skill. We consider amulti-level redundancy allocation problem in which all cases of redundancy for system, module, and component levels areconsidered. A tabu search of memory-based mechanisms that balances intensification with diversification via the short-term andlong-term memory is proposed for its solution. To the best of our knowledge, this is the first attempt to use a tabu search forthis problem. Our tabu search algorithm is compared with the previous genetic algorithm for the problem on the new composedtest problems as well as the benchmark problems from the literature. Computational results show that the proposed methodoutstandingly outperforms the genetic algorithm for almost all test problems.
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