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.
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