# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
###### Abstract  
Metaheuristic algorithms have gained popularity in the past decade due to their remarkable ability to address various optimization challenges. Among these, the JAYA algorithm has emerged as a recent contender that demonstrates strong performance across different optimization problems, largely attributed to its simplicity. However, real-world problems have become increasingly complex in today's era, creating a demand for more robust and effective solutions to tackle these intricate challenges and achieve outstanding results. This article proposes an enhanced JAYA (EJAYA) method that addresses its inherent shortcomings, resulting in improved convergence and search capabilities when dealing with diverse problems. The current study evaluates the performance of the proposed optimization methods on both continuous and discontinuous problems. Initially, EJAYA is applied to solve 20 prominent test functions and is validated by comparison with other contemporary algorithms in the literature, including moth-flame optimization, particle swarm optimization, the dragonfly algorithm, and the sine-cosine algorithm. The effectiveness of the proposed approach in discrete scenarios is tested using feature selection and compared to existing optimization strategies. Evaluations across various scenarios demonstrate that the proposed enhancements significantly improve the JAYA algorithm's performance, facilitating escape from local minima, achieving faster convergence, and expanding the search capabilities.  
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of the gray wolf optimization (GWO) algorithm inspired the design of another optimization algorithm [9]. The following algorithms are also inspired by various natural behaviors: the Adolescent Identity Search Algorithm (AISA) [10], Butterfly Optimization Algorithm (BOA) [11; 12], Ant Lion Optimizer (ALO) [13], Cooperation Search Algorithm (CS) [14], Equilibrium Optimizer (EO) [15], Crow Search Algorithm (CSA) [16], Optimization Booster Algorithm (OBA) [17], Honey Badger Algorithm (HBA) [18], African Vultures Optimization Algorithm (AVOA) [19], Rain Optimization Algorithm (ROA) [20], Tunicate Swarm Algorithm (TSA) [21], Pity Beetle Algorithm (PBA) [22], and Queuing Search Algorithm (QSA) [23].  
Based on the review of the aforementioned papers, it is evident that there is a compelling demand for the development of a novel optimization algorithm. This algorithm should aim to reduce the dependence on algorithm-specific parameters while effectively revealing the most optimal solution. Additionally, it is noteworthy that a substantial portion of the evaluations conducted in these studies primarily focused on specific problem domains, categorized as either continuous or discrete optimization problems. Consequently, the need arises for the creation of an algorithm capable of addressing the intricacies of both continuous and discrete scenarios.  
Accordingly, the current study proposes a novel modified JAYA algorithm with enhanced search capability and convergence. Scientifically, an algorithm with balanced exploration and exploitation capabilities must be designed. This balance allows optimizers to develop a more dependable algorithm with superior performance compared to the basic version. The performance of this approach is then compared with other recent algorithms in continuous and discontinuous problem domains. Various benchmark functions are considered to assess the proposed method's ability to handle continuous problems. Regarding discontinuous problems, one of the most popular and applicable scenarios, feature selection, is addressed. Feature selection can be defined as an optimization problem aimed at extracting an optimal subset from large-scale data.