# Modified Coral Reef Optimization Methods for Job Shop Scheduling Problems
## 2 Related Work
Among population-based algorithms, nature-inspired optimization algorithms are ubiquitous in terms of ease of implementation and superior searchability [24]. For instance, the GA (genetic algorithm--inspired by evolution) [25; 26], PSO (particle swarm optimization--influenced by swarm intelligence) [27], and SA (simulated annealing--inspired by metal cooling behavior) [28] are among the most reliable and effective algorithms accessible. Significantly, meta-heuristic algorithms differ from blindly random search algorithms in that randomness is used intelligently and biasedly, making them the current research trend for solving difficult and complex issues. In addition, combination metaheuristic approaches have been established to leverage the capabilities within each method to obtain more robust and exhaustive optimization strategies. Modern metaheuristic algorithms also incorporate various mathematical models and analytical operational techniques to deliver greater performance. [PERSON] et al. propose a metaheuristic algorithm that combines GA with a disjunctive mathematical model and employs the open-source solution Coin-OR Branch and Cut to optimize the JSSP [12]. By combining an open-source solver with genetic algorithm, the metaheuristic approach enables the development of efficient solutions and reduces computation time. [PERSON] et al. suggested employing a guidance operator assigned to changing ill-adapted individuals utilizing genetic material from well-adapted individuals to enhance the GA population [13]. The results indicate the new algorithm achieves a result 45.88% better than the old approach. [PERSON] et al. introduce a novel metaheuristic algorithm capable of guiding the search process to promising regions based on the expected value affected by the performance of applicant samples and the growth rate of the candidate solutions region, called search economics for the job shop scheduling problem (SEJSP) [29]. SEJSP also produced positive experimental findings when attempting to resolve JSSP. In recent years, solving a fundamental problem such as JSSP has been approached in the direction of enhancing classical metaheuristic algorithms or combining them with other methodologies. [PERSON] et al. [30] improve PSO to NGPSO by incorporating nonlinear inertia weight and Gaussian mutation to handle JSSP. [PERSON] proposed the GA-CPG-GT method using the GA algorithm with uniform crossover paired with the Giffler and Thompson algorithm and yielded positive results when addressing JSSP [31]. [PERSON] and [PERSON] utilized Gray Wolf Optimization (GWO) algorithm, inspired by the gray wolves' social hierarchy and hunting behaviors, to solve JSSP [32], and [PERSON] et al. developed them with some modifications to establish the Discrete Wolf Pack Algorithm (DWPA) and achieved numerous exciting results in investigating JSSP [33]. In another direction, [PERSON] et al. deployed a novel meta-heuristic technique called Single Seekers Society (SSS) to manage JSSP effectively [34]. The coral reef optimization approach (CRO) is one of the complex bio-inspired computation methods used to solve engineering and science problems by simulating the \"formation\" and \"reproduction\" of corals in coral reefs. The approach was first envisioned by [PERSON] et al. in 2014 [14]. Since then, it has been utilized in various relevant topics, including optimal mobile network deployment [15], enhanced battery scheduling of microgrids [16], and wind speed prediction systems with success in renewable energy in \"Off-shore Wind Farm Design\" [17]. Various hybrid algorithms based on the original version have evolved to facilitate better performance while diminishing computing time. For example, in 2016, a combination of CRO and a variable neighborhood search method was applied to unequal area facility layout problems [35]. Alternatively, another hybrid CRO technique takes advantage of Spark's MapReduce programming paradigm to reduce the system's overall response time and numerous other fascinating applications [36].