Constrained Reliability Redundancy Optimization of Complex Systems using Genetic Algorithm

The paper presents a Genetic Algorithm (GA) approach for solving constrained reliability redundancy optimization of general systems. The advanced GA technique uses a dynamic adaptive penalty function to consider the infeasible solutions also and guides the search to optimal or near optimal solution. The penalty technique is applied to keep a certain amount of infeasible solutions in each generation so as to enforce genetic search towards an optimal solution from both the sides of feasible and infeasible regions. The performance of the method is compared with GA tool Box of MATLAB.

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