Research Article
Hybrid Coral Reef Optimization Algorithm Employed Local Search Technique for Job Shop Scheduling Problems
@INPROCEEDINGS{10.1007/978-3-031-33614-0_15, author={Chin-Shiuh Shieh and Thanh-Tuan Nguyen and Dinh-Cuong Nguyen and Thanh-Nghia Nguyen and Mong-Fong Horng and Denis Miu}, title={Hybrid Coral Reef Optimization Algorithm Employed Local Search Technique for Job Shop Scheduling Problems}, proceedings={Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings}, proceedings_a={BDTA}, year={2023}, month={5}, keywords={Job-shop scheduling coral reef optimization local search hybrid approach}, doi={10.1007/978-3-031-33614-0_15} }- Chin-Shiuh Shieh
Thanh-Tuan Nguyen
Dinh-Cuong Nguyen
Thanh-Nghia Nguyen
Mong-Fong Horng
Denis Miu
Year: 2023
Hybrid Coral Reef Optimization Algorithm Employed Local Search Technique for Job Shop Scheduling Problems
BDTA
Springer
DOI: 10.1007/978-3-031-33614-0_15
Abstract
The JSSP (job shop scheduling problem) is a crucial problem in operational research with certain real-world applications. Due to the fact that the JSSP is an NP-hard (nondeterministic polynomial time) issue, approximation techniques are frequently employed to solve it. This paper introduces a novel biologically-inspired metaheuristic algorithm called Coral Reef Optimization (CRO) in combination with local search strategies Simulated Annealing (SA) significantly improves performance and solution-finding speed. The performance of hybrid algorithms is examined by solving various instances of JSSP. The results indicate that local search methods greatly improve the search efficiency of the hybrid algorithm in comparison to the original algorithm, which was used to assess the improvement. Moreover, comparative findings with five state-of-the-art algorithms from the literature demonstrate that the proposed hybrid algorithms have advantageous search capabilities.
