A multi-objective approach to redundancy allocation problem in parallel-series systems

Authors:
Zai Wang;Tianshi Chen;Ke Tang;Xin Yao
Affiliations:
Nature Inspired Computation and Applications Laboratory, The Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China;Nature Inspired Computation and Applications Laboratory, The Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China;Nature Inspired Computation and Applications Laboratory, The Department of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China;Nature Inspired Computation and Applications Lab., The Dept. of Comp. Sci. and Techn., Univ. of Sci. and Techn. of China, Hefei, Anhui, China and CERCIA, The school of Comp. Sci., Univ. of Birming ...
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

The Redundancy Allocation Problem (RAP) is a kind of reliability optimization problems. It involves the selection of components with appropriate levels of redundancy or reliability to maximize the system reliability under some predefined constraints. We can formulate the RAP as a combinatorial problem when just considering the redundancy level, while as a continuous problem when considering the reliability level. The RAP employed in this paper is that kind of combinatorial optimization problems. During the past thirty years, there have already been a number of investigations on RAP. However, these investigations often treat RAP as a single objective problem with the only goal to maximize the system reliability (or minimize the designing cost). In this paper, we regard RAP as a multi-objective optimization problem: the reliability of the system and the corresponding designing cost are considered as two different objectives. Consequently, we can utilize a classical Multi-objective Evolutionary Algorithm (MOEA), named Non-dominated Sorting Genetic Algorithm II (NSGA-II), to cope with this multi-objective redundancy allocation problem (MORAP) under a number of constraints. The experimental results demonstrate that the multi-objective evolutionary approach can provide more promising solutions in comparison with two widely used single-objective approaches on two parallel-series systems which are frequently studied in the field of reliability optimization.