Shuffled frog-leaping algorithm for order acceptance and scheduling in flow shop

Lei, Deming; Tan, Xianfeng

doi:10.1109/chicc.2016.7554855

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e genetic-shuffled frog-leaping algorithm (GSFLA) is a swarm intelligence optimization algorithm that simulates the process of frog foraging behavior [30][31][32][33][34]. It combines the advantages of the memetic algorithm (MA) based on memetic evolution [35] and the particle swarm optimization algorithm (PSO) based on swarm behavior [36].…”

Section: Introductionmentioning

confidence: 99%

An Improved Genetic-Shuffled Frog-Leaping Algorithm for Permutation Flowshop Scheduling

Yang

Chen

et al. 2020

Complexity

View full text Add to dashboard Cite

Due to the NP-hard nature, the permutation flowshop scheduling problem (PFSSP) is a fundamental issue for Industry 4.0, especially under higher productivity, efficiency, and self-managing systems. This paper proposes an improved genetic-shuffled frog-leaping algorithm (IGSFLA) to solve the permutation flowshop scheduling problem. In the proposed IGSFLA, the optimal initial frog (individual) in the initialized group is generated according to the heuristic optimal-insert method with fitness constrain. The crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal solutions and accelerate the evolution. To evolve the frogs with the same optimal fitness more outstanding, the disturbance mechanism is applied to obtain the optimal frog of the whole group at the initialization step and the optimal frog of the subgroup at the searching step. The mathematical model of PFSSP is established with the minimum production cycle (makespan) as the objective function, the fitness of frog is given, and the IGSFLA-based PFSSP is proposed. Experimental results have been given and analyzed, showing that IGSFLA not only provides the optimal scheduling performance but also converges effectively.

show abstract