RELIABILITY OPTIMIZATION OF SYSTEMS USING A GENETIC ALGORITHM

Abstract

After initial production, improvements are often made to components of a system to upgrade the system performance. The Problem is to select components and redundancy levels to optimize some objective function; given system level constraints on reliability, cost and weight A problem specific Genetic Algorithm (GA) is developed to analyze series-parallel system and to determine the optimal design configuration. Previous formulation of the problem have implicit restrictions concerning the type of redundancy allowed, the number of available components choices and whether mixing of components is allowed.GA is a robust evolutionary optimization search technique with very few restrictions concerning the type or size of the design problem. Results and comparisons show that Genetic Algorithm performs very well.GA find the optimal solutions better than the Lagrange multipliers method. The integration of Genetic Algorithm optimization capabilities with reliability analysis can provide a robust, powerful design for reliability tool.