文件名称:A-hybrid-cuckoo-search-and-genetic-algorithm-for-
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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.
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.
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A hybrid cuckoo search and genetic algorithm for reliability–redundancy.pdf