Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Lévy flights, non-local search and simulated annealing
Journal of Computational Physics
A novel global harmony search algorithm for reliability problems
Computers and Industrial Engineering
Nature-Inspired Metaheuristic Algorithms: Second Edition
Nature-Inspired Metaheuristic Algorithms: Second Edition
Short communication: An effective global harmony search algorithm for reliability problems
Expert Systems with Applications: An International Journal
Solving reliability redundancy allocation problems using an artificial bee colony algorithm
Computers and Operations Research
A novel quantum inspired cuckoo search for knapsack problems
International Journal of Bio-Inspired Computation
Expert Systems with Applications: An International Journal
Optimal design for software reliability and development cost
IEEE Journal on Selected Areas in Communications
Multi-objective reliability-redundancy allocation problem using particle swarm optimization
Computers and Industrial Engineering
Improved cuckoo search for reliability optimization problems
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Computational Optimization and Applications in Engineering and Industry
Computational Optimization and Applications in Engineering and Industry
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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 from 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.