Diversity Operators-based Artificial Fish Swarm Algorithm to Solve Flexible Job Shop Scheduling Problem
Main Article Content
Abstract
Artificial fish swarm algorithm (AFSA) is one of the critical swarm intelligent algorithms. In this
paper, the authors decide to enhance AFSA via diversity operators (AFSA-DO). The diversity operators will
be producing more diverse solutions for AFSA to obtain reasonable resolutions. AFSA-DO has been used to
solve flexible job shop scheduling problems (FJSSP). However, the FJSSP is a significant problem in the
domain of optimization and operation research. Several research papers dealt with methods of solving this
issue, including forms of intelligence of the swarms. In this paper, a set of FJSSP target samples are tested
employing the improved algorithm to confirm its effectiveness and evaluate its execution. Finally, this paper
concludes that the enhanced algorithm via diversity operators has discrepancies about the initial AFSA, and
it also provided both sound quality resolution and intersected rate.
Received 28/12/2021
Revised 1/11/2022
Accepted 3/11/2022
Published Online First 20/3/2023
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Zhu Z, Zhou X. An efficient evolutionary grey wolf optimizer for multi-objective flexible job shop scheduling problem with hierarchical job precedence constraints. Comput Ind Eng. 2020; 140: 106280. https://doi.org/10.1016/j.cie.2020.106280
Gao D, Wang G, Pedrycz W. Solving Fuzzy Job-Shop Scheduling Problem Using DE Algorithm Improved by a Selection Mechanism. IEEE Trans Fuzzy Syst. 2020; 28(12):3265 - 3275. https://doi.org/10.1109/TFUZZ.2020.3003506
Bharti P, Jain S. Hybrid frameworks for flexible job shop scheduling. Int J Adv Manuf Technol. 2020; 108(5-6): 1563–1585. https://doi.org/10.1007/s00170-020-05398-4
Abu-Srhahn A, Al-Hasan M. Hybrid Algorithm using Genetic Algorithm and Cuckoo Search Algorithm for Job Shop Scheduling Problem. Int J Comput Sci. 2015; 12(2): 288-292.
Salem I E, Mijwil M M, Abdulqader A W, Ismaeel M M. Flight-Schedule using Dijkstra's Algorithm with Comparison of Routes Finding. Int J Electr Comput. 2022; 12(2): 1675-1682. http://doi.org/10.11591/ijece.v12i2.pp%25p.
Dehghan-Sanej K, Eghbali-Zarch M, Tavakkoli-Moghaddam R, Sajadi SM, Sadjadi SJ. Solving a new robust reverse job shop scheduling problem by meta-heuristic algorithms. Eng Appl Artif Intell. 2021 May; 101: 104207. https://doi.org/10.1016/j.engappai.2021.104207.
HNK Al-behadili. Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering. Baghdad Sci J. 2022 April; 19(2): 409-421. https://dx.doi.org/10.21123/bsj.2022.19.2.0409.
Liu Z, Wang J, Zhang C, Chu H, Ding G, Zhang L. A hybrid genetic-particle swarm algorithm based on multilevel neighbourhood structure for flexible job shop scheduling problem. Comput Oper Res 2021; 135: 105431. https://doi.org/10.1016/j.cor.2021.105431
Osaba E, Villar-Rodriguez E, Ser J D, Nebro A J, Molina D, LaTorre A, et al. A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems. Swarm Evol Comput. 2021; 64: 100888. https://doi.org/10.1016/j.swevo.2021.100888
Yasear, S A, Ku-Mahamud K R. Taxonomy of Memory Usage in Swarm Intelligence-Based Metaheuristics. Baghdad Sci J. 2019; 16: 445–452. https://doi.org/10.21123/bsj.2019.16.2(SI).0445.
Mijwil M M, Abttan R A. Applying Genetic Algorithm to Optimization Second-Order Bandpass MGMFB Filter. Pertanika J Sci Technol. 2020; 28 (4): 1413–1425. https://doi.org/10.47836/pjst.28.4.15
Wang Y, Han J. A FJSSP Method Based on Dynamic Multi-Objective Squirrel Search Algorithm. Int J Antennas Propag. 2021; Article ID 6062689: 1-19. https://doi.org/10.1155/2021/6062689
Toshev L. A. Hybrid PSO and TS Algorithm for FJSSP. 10th Natl. Conf. Int. Particip. ELECTRON. 2019 - Proc. 1-6, Sofia, Bulgaria. https://doi.org/10.1109/Electronica.2019.8825612
Alobaidi A T S, Hussein S A. An improved Artificial Fish Swarm Algorithm to solve flexible job shop. Ann Conf on New Trends in Info Comm Tech Appli. 2017; 1-6, Baghdad, Iraq. https://doi.org/10.1109/NTICT.2017.7976155
Al-Obaidi A T S, Abdullah H S, Ahmed Z O. Camel Herds Algorithm: A New Swarm Intelligent Algorithm to Solve Optimization Problems. I J P C C. 2017; 3(1): 6-10. https://doi.org/10.31436/ijpcc.v3i1.44
Al-Obaidi A T S, Hussein S A. Two Improved Cuckoo Search Algorithm to Solve Flexible Job Shop Scheduling Problem. I J P C C. 2016; 2(2): 25-31.
Shaker A S, Abdulqader A W, Mijwil M M. DE-striping hype spectral Remote Sensing Images using Deep Convolutional Neural Network. Asian J Appl Sci 2021; 9 (4): 285-290. https://doi.org/10.24203/ajas.v9i4.6719
Pythaloka D, Wibowo A T, Sulistiyo M D. Artificial fish swarm algorithm for job shop scheduling problem. In Proc on Int Conf Inf Commun Technol. 2015; 1-6, Nusa Dua, Bali, Indonesia, https://doi.org/10.1109/ICoICT.2015.7231465
Feng Y, Zhao S, Liu H, Analysis of Network Coverage Optimization Based on Feedback K-Means Clustering and Artificial Fish Swarm Algorithm. IEEE Acc 2020; 42864 - 42876. https://doi.org/10.1109/ACCESS.2020.2970208
Xu H, Zhao Y, Ye C, Lin F. Integrated optimization for mechanical elastic wheel and suspension based on an improved artificial fish swarm algorithm. Adv Eng softw. 2019; 137: 102722. https://doi.org/10.1016/j.advengsoft.2019.102722
Li X, Keegan B, Mtenzi F. Energy Efficient Hybrid Routing Protocol Based on the Artificial Fish Swarm Algorithm and Ant Colony Optimisation for WSNs. Sens. 2018; 18(10): 3351, https://doi.org/10.3390/s18103351
Ma C, He R. Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm. Neural Comput Appl. 2019; 31: 2073–2083.https://doi.org/10.1007/s00521-015-1931-y
Zheng Z, Li J, Duan P. Optimal chiller loading by improved artificial fish swarm algorithm for energy saving. Math Comput Simul. 2019; 155: 227-243. https://doi.org/10.1016/j.matcom.2018.04.013
Jia B, Hao L, Zhang C, Huang B. A Privacy-sensitive Service Selection Method Based on Artificial Fish Swarm Algorithm in the Internet of Things. Mob Netw Appl 2020; 26: 1523–1531. https://doi.org/10.1007/s11036-019-01488-0
Zong X, Wang C, Du J, Jiang Y. Tree hierarchical directed evacuation network model based on artificial fish swarm algorithm. Int J Mod Phys C 2019; 30(11): 195C097. https://doi.org/10.1142/S0129183119500979
Tan W, Mohamad-Saleh J. Normative fish swarm algorithm (NFSA) for optimization. Soft Comput. 2019; 24: 2083–2099. https://doi.org/10.1007/s00500-019-04040-0
Hurink J, Jurisch B, Thole M. Tabu search for the job-shop scheduling problem with multi-purpose machines. OR Spectr. 1994; 15: 205-215.