RELIABILITY OPTIMIZATION USING GENETIC ALGORITHM

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. The primary goal of a reliability design is to improve the system reliability. In the initial design activity, a redundancy allocation is a direct way of enhancing the system reliability. 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 dissertation is that kind of combinatorial optimization problem in series system. This problem is treated single objective problem, and the only goal is to maximize system reliability and solved by NLP methods these belong to classical method and same problem solved by GA also and compare their results, where the resulting solution can be rounded off to yield integer solution. 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 dissertation, 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 parallel-series systems which are frequently studied in the field of reliability optimization.