Analysis of Measurement & the Sensitivity for Multi State System Reliability With Genetic Algorithm Approach (GAA)

In this paper, I am described the measurement and sensitivity of Multi-state System (MSS) with genetic algorithms. In the field of reliability analysis, this paper’s work is on the binary state system. I am considered that three measures are mostly used as MSS availability, MSS expected performance and MSS expected unsupplied demand. According to my concept, the MSS reliability is formulated and its structure function is defined for a coherent MSS, such as Boolean model are extended for the multi valued case, the stochastic process approach, Monto-Carlo simulation. In optimal allocation problems, I am finding the quality of a solution is the only information available during the search for the optimal solution. So, I have to use the heuristic algorithm for solving this problem and developing the family of genetic algorithms (GA) which is based on the simple principle of evolutionary search. This multi-objective genetic algorithm uses the universal moment generating function approach to evaluate the different reliability or availability indices of the system. The components are characterized by having different performance type, rate and reliability. In this paper, concept is presented describing GA developed for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity. Key WordsReliability, Availability, Genetic algorithms, Multi-objective optimization, Heuristic algorithm, Universal generating function (UGF).