Abstract
ABSTRACTA Cuckoo Search (CS) algorithm based on ant colony algorithm is proposed for scheduling problem in permutation flow shop scheduling problem (PFSP). When the raised CS algorithm obtains the position of the bird nest to be updated, it is used as a set of initial solution of the ant colony optimization algorithm (ACO), and ACO algorithm search optimization is performed in a very small range. After that, the solution obtained by the ACO search is taken as a new candidate solution, compared with the candidate bird nest according to the fitness degree. When the candidate solution of the ACO search optimization is better than the one generated by the Lévy flight, the latter is replaced. Finally, the CS algorithm is selected, changing the new bird nest position according to the abandonment probability. The updated position tends to be more optimal, which improves the quality of the solution as well as the convergence speed and accuracy of the algorithm. Comparing the performance of the proposed algorithm with the standard Cuckoo one, by testing function, the optimized performance was verified. Finally, the Car benchmark test served as test data, and the performance in the PFSP was compared. The effectiveness and superiority in the algorithm in solving problem were confirmed.
Highlights
The permutation flow shop scheduling problem (PFSP), based on constraint of the scheduling problem, further increases the constrained scheduling problem that all workpieces have the same processing order in any machine, which effectively enhances the enterprises’ efficiency (Fernandez-Viagas & Framinan, 2017)
When the raised Cuckoo Search (CS) algorithm obtains the position of the bird nest to be updated, it is used as a set of initial solution of the ant colony optimization algorithm (ACO), and ACO algorithm search optimization is performed in a very small range
When the CS algorithm finds that the optimal solution is every iterated, in other words, when Lévy flight is to obtain the bird nest position to be updated, it is introduced as a set of initial solutions into the ACO search algorithm as well as the initial one of the ACO algorithm
Summary
The permutation flow shop scheduling problem (PFSP), based on constraint of the scheduling problem, further increases the constrained scheduling problem that all workpieces have the same processing order in any machine, which effectively enhances the enterprises’ efficiency (Fernandez-Viagas & Framinan, 2017). The algorithm utilizes ACO algorithm to optimize the position update strategy of CS, making the candidate solution evolve towards the optimal one with the iteration, so as to address the problems of slow convergence speed and low convergence precision caused by the random position update of CS algorithm. On this basis, the algorithm is applied to settle the optimization design problem of PFSP, and the effectiveness of the proposed algorithm is verified by Car benchmark class
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