Abstract
Reliability indicates the probability implementing specific performance or function of products and achieving successfully the objectives within a time schedule under a certain environment. The optimization of system reliability plays an important role in systems maintenance planning and logistics requirements. In order to achieve more reliable systems, using redundancy is the most widely used approach in complex technical design. Solutions to those problems intend to identify the optimal combination of component selections and redundancy levels given constraints on the overall system. In general, reliability optimization problems are nonlinear programming problems and proved to be NP-hard from computation point of view. Recently, a class of heuristic search strategies, known as computational intelligence (CI), has emerged to solve the problems due to their ability to find an almost global optimal solution in a reasonable time. This paper presents an overview of the various CI methods to solve the reliability optimization problems. Guidelines for the successful use and implementation of reliability optimization are discussed and several decision variables are described that can be used to distinguish between different reliability problem types. Based on this review, several opportunities to improve and extend the current research are showed.
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