# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 2 Literature Survey  
Population-based algorithms possess inherent strength in problem solving, and researchers frequently find ways to enhance their effectiveness by addressing their intrinsic limitations. These modifications enable the algorithms to thoroughly explore the problem space, achieve quicker convergence, and gain other benefits. Various enhancements have been introduced thus far to improve the performance of these algorithms. For instance, enhanced particle swarm optimization [24; 25; 26], the improved flower pollination algorithm [27], improved whale optimization [28], the improved scatter search algorithm [29], the improved differential evolution algorithm [30], the improved bat algorithm [31], improved harmony search [32; 33; 34; 35], the improved firefly algorithm [36], the improved bacteria foraging algorithm [37], the improved artificial bee colony algorithm [38; 39; 40], the improved genetic algorithm [41; 42; 43], the improved simulated annealing algorithm [44; 45], the modified squirrel search algorithm [46], the improved firework algorithm [47], improved gray wolf optimization [48], and the improved sine-cosine algorithm [49]. Although the algorithms mentioned above, with their modifications, have their benefits, they must be tuned using algorithm-specific parameters. For example, the GA necessitates setting parameters such as the crossover probability, selection operator, and mutation probability. Similarly, the other algorithms listed in the preceding paragraphs have specific parameters that must be set for optimal performance. These parameters are unique to each algorithm and should be adjusted along with the common parameters (i.e., the number of decision variables and the maximum number of populations/iterations). The general control parameters are standard among all algorithms. However, the algorithm-specific parameters are unique and must also be defined. These factors have a significant impact on the performance of metaheuristic-based algorithms. Properly configuring these settings is critical because, if the parameters are not tuned correctly, it may increase the computational burden or lead to a preference for a local optimum solution. A parameterless approach, the JAYA algorithm, has recently been presented to address the issue of establishing algorithm-specific parameters [50]. JAYA is an advanced and robust algorithm that draws inspiration from the principle of prioritizing the best member among candidate solutions while simultaneously disregarding the worst member [50]. Despite the notable benefits offered by this algorithm, such as the absence of parameter settings and rapid convergence, it faces challenges in adequately exploring the search space and occasionally becoming trapped in local minima. In other words, because it employs a single learning technique with a low population diversity, JAYA is vulnerable to being caught in local optima when confronted with complex optimization problems. Technically, as the population undergoes updates through a single position change, the diversity within the population diminishes. This reduction in diversity limits the algorithm's ability to explore a broader range of potential solutions, ultimately decreasing its effectiveness in discovering optimal or near-optimal solutions in the search space. Maintaining diversity is crucial for avoiding premature convergence and ensuring that the algorithm does not become trapped in local optima, thus enhancing its exploratory capabilities. For instance, in the GA, applying mutation operators can help preserve diversity and prevent premature convergence, while, in PSO, balancing exploration and exploitation through velocity control can ensure broader search space exploration. This limitation hampers achieving high-quality results when dealing with significantly large problem sizes. To address these drawbacks, various attempts have been made.