Discrete cuckoo search algorithms for two-sided robotic assembly line balancing problem
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Robotics are extensively utilized in modern industry to replace human labor and achieve high automation and flexibility. In order to produce large-size products, two-sided assembly lines are widely applied, where robotics can be employed to operate tasks on workstations. Since the applied traditional optimization methods are limited, the current work presented a new discrete cuckoo search algorithm to solve the two-sided robotic assembly line balancing problem. The original cuckoo search algorithm was modified by employing neighbor operations. Furthermore, a new procedure to generate individuals to replace the abandoned nests was developed to enhance the intensification. Since the considered problem has two subproblems, namely the robot allocation and assembly line balancing, the present work extended the cuckoo search algorithm to cooperative coevolutionary paradigm by dividing the cuckoos into two sub-swarms, each addressing a subproblem. In order to emphasize the exploration, a restart mechanism was employed. The proposed discrete algorithm’s evolution process and convergence were compared with another two popular optimization algorithms, namely the genetic algorithm and particle swarm optimization algorithm. Computational study on the proposed algorithms and other five recent algorithms along with statistical analysis demonstrated that the proposed methods yielded promising results.
KeywordsAssembly line balancing Two-sided robotic assembly line Cuckoo search Cooperative coevolution Evolutionary algorithms Genetic algorithm Particle swarm optimization
This research work is funded by the National Natural Science Foundation of China (Grant No. 51275366) (Qiuhua Tang).
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