Ant colony optimization for Cuckoo Search algorithm for permutation flow shop scheduling problem

Abstract

A 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.

Keywords

• Lévy flight
• Cuckoo Search algorithm
• dynamic balance factor
• self-adaptive step
• permutation flow shop scheduling problem