Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted

increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm

cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS–GA is proposed

to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard

CS, the balance between the exploration and exploitation ability further improved and more search

space are observed during the algorithms’ performance. The computational results carried out on four

classical reliability–redundancy allocation problems taken the literature confirm the validity of

the proposed algorithm. Experimental results are presented and compared with the best known solutions.

The comparison results with other evolutionary optimization methods demonstrate that the proposed

CS–GA algorithm proves to be extremely effective and efficient at locating optimal solutions.-Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted

increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm

cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS–GA is proposed

to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard

CS, the balance between the exploration and exploitation ability further improved and more search

space are observed during the algorithms’ performance. The computational results carried out on four

classical reliability–redundancy allocation problems taken　the literature confirm the validity of

the proposed algorithm. Experimental results are presented and compared with the best known solutions.

The comparison results with other evolutionary optimization methods demonstrate that the proposed

CS–GA algorithm proves to be extremely effective and efficient at locating optimal solutions.