Solving continuous optimization problems using the ımproved Jaya algorithm (IJaya)

Jaya algorithm is one of the heuristic algorithms developed in recent years. The most important difference from other heuristic algorithms is that it updates its position according to its best and worst position. In addition to its simplicity, there is no algorithm-specific parameter. Because of these advantages, it has been preferred by researchers for problem-solving in the literature. In this study, the random walk phase of the original Jaya algorithm is developed and the Improved Jaya Algorithm (IJaya) is proposed. IJaya has been tested for success in eighteen classic benchmark test functions. Although the performance of the original Jaya algorithm has been tested at low dimensions in the literature, its success in large sizes has not been tested. In this study, IJaya's success in 10, 20, 30, 100, 500, and 1000 dimensions was examined. Also, the success of IJaya was tested in different population sizes. It has been proven that IJaya's performance has increased with the tests performed. Test results show that IJaya displays good performance and can be used as an alternative method for constrained optimization. In addition, three different engineering design problems were tested in different population sizes to demonstrate the achievements of Jaya and IJaya. According to the results, IJaya can be used as an optimization algorithm in the literature for continuous optimization and large-scale optimization problems.