This paper aims to solve Redundancy allocation problem (RAP). It is a significant complex optimization and non-linear integer programming problem of reliability engineering. RAP includes the choices of components and the suitable amount of redundant subsystems for maximizing reliability of the system under given restrictions like cost, weight, volume etc. It is difficult to solve non-linear complex problems. In this paper, the RAP is solved by the combination of genetic and simulating algorithm that is called Hybrid Genetic Simulating Annealing Algorithm (HGSAA). It can be observed that superiority of both the algorithms are combined and form an adequate algorithm which ignores the individual weakness. Comparative analysis of HGSAA with existing methods such as Heuristic Algorith, Constraint Optimization Genetic Algorithm, Hybrid Particle Swarm Optimization and Constraint Optimization Genetic Algorithm are presented in this study. RAP is also solved by Branch and Bound method to validate the result of HGSAA. The developed algorithm is programmed by Matlab.

本文旨在解决冗余分配问题（RAP）。这是可靠性工程中的一个重要的复杂优化和非线性整数规划问题。RAP包括组件的选择和适当数量的冗余子系统，以在给定的限制（例如成本，重量，体积等）下最大化系统的可靠性。解决非线性复杂问题非常困难。在本文中，RAP通过遗传和模拟算法的组合来解决，该算法称为混合遗传模拟退火算法（HGSAA）。可以看出，两种算法的优越性相结合，形成了忽略个体弱点的适当算法。HGSAA与现有方法（例如启发式算法，约束优化遗传算法，提出了混合粒子群算法和约束优化遗传算法。RAP还可以通过Branch and Bound方法求解，以验证HGSAA的结果。所开发的算法由Matlab编程。