Redundancy allocation problem (RAP) is a non-linear programming problem which is very difficult to solve through existing heuristic and non-heuristic methods. In this research paper, three algorithms namely heuristic algorithm (HA), constraint optimization genetic algorithm (COGA) and hybrid genetic algorithm combined with particle swarm optimization (HGAPSO) are applied to solve RAP. Results obtained from individual use of genetic algorithm (GA) and particle swarm optimization (PSO) encompass some shortcomings. To overcome the shortcomings with their individual use, HGAPSO is introduced which combines fascinating properties of GA and PSO. Iterative process of GA is used by this hybrid approach after fixing initial best population from PSO. The results obtained from HA, COGA and HGAPSO with respect to increase in reliability are 50.76, 47.30 and 62.31 respectively and results with respect to CPU time obtained are 0.15, 0.209 and 3.07 respectively as shown in Table 3 of this paper. COGA and HGAPSO are programmed by Matlab.

中文翻译：

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遗传和粒子群混合算法：冗余分配问题

冗余分配问题（RAP）是一个非线性规划问题，很难通过现有的启发式和非启发式方法解决。本文采用启发式算法（HA），约束优化遗传算法（COGA）和混合遗传算法结合粒子群优化算法（HGAPSO）三种算法求解RAP。单独使用遗传算法（GA）和粒子群优化（PSO）获得的结果包含一些缺陷。为了克服单独使用的缺点，HGAPSO引入了GA和PSO的迷人特性。在从PSO确定初始最佳种群之后，此混合方法使用了GA的迭代过程。从HA，COGA和HGAPSO获得的可靠性增加结果是50.76，47。如本文表3所示，分别获得30和62.31的结果，并且获得的有关CPU时间的结果分别为0.15、0.209和3.07。COGA和HGAPSO由Matlab编程。