# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 2 Literature Survey
One such approach involves converting the JAYA population into a subpopulation, aiming to improve the algorithm's efficiency [51], including greater search capability and better convergence. However, because the algorithm is sliced into several groups, it adds more complexity to the algorithm, such as difficulty in implementation, increased time consumption, etc. Similarly, a dynamic weight parameter acting as a varying coefficient was incorporated in JAYA to enhance its convergence [52]. Although the algorithm's performance experienced a significant improvement, some new parameters were added, making the algorithm sensitive to parameter settings. Additionally, integrating the shuffling process with JAYA was employed to achieve enhanced exploration within the search domain. A clustering technique was also used to replace solutions that exhibited the lowest quality [53]. Recently, a hybrid JAYA algorithm with a CSA was provided by [PERSON] and colleagues. This algorithm revealed excellent efficiency in solving optimization benchmarks [54]. Nevertheless, it suffers from higher time consumption and increased parameter tuning due to integration with the CSA, resulting in nearly two times the time required by the original JAYA. [PERSON] and [PERSON] introduced a modified algorithm by combining the population update of differential evolution with the original version of JAYA [55]. This integration enhanced the global search ability. Nevertheless, its complexity increased substantially due to the mixing of the two algorithms and the addition of new parameters. JAYA was also integrated with Tabu search for solving the flexible job shop scheduling problem. In this case, hybridization resulted in increased time consumption and additional parameter tuning [56]. Given the complexity and ruggedness of some optimization problems, researchers have modified the JAYA algorithm to enhance its convergence behavior. Many have combined JAYA with other powerful optimization techniques, leading to the development of various JAYA algorithm variants. These include binary JAYA [57], self-adaptive JAYA [58; 59], elitism-based JAYA [60], elitism-based self-adaptive multi-population JAYA [61], chaotic JAYA [62], neural network JAYA [63], three metaphorless simple algorithms for solving optimization problems [64], the concept of search space division [65], and a review of metaheuristic algorithms on 57 benchmarks [66]. From the provided literature review, the main contributions of this proposal are briefly outlined as follows:  
* The proposed approach is based on modifying the JAYA algorithm, which results in improved search capability, faster convergence, and higher-quality solutions. * To address continuous problems, the proposed approach is evaluated across 20 widely recognized benchmark functions. The results obtained from these benchmark functions are compared to those achieved by other existing algorithms, including the grasshopper optimization algorithm (GOA), dragonfly algorithm (DA), moth-flame optimization (MFO), and others. * Feature selection is also considered as a discrete problem. The proposed method is then applied to solve this problem, and its findings are compared to other algorithms in the literature, such as the genetic algorithm, particle swarm optimization algorithm, etc.