Performance Validation of Jaya Algorithm to The Most Well-known Testbench Problem

Abstract

Soft computing algorithms are population-based, probabilistic, that have the same common controlling parameters such as population size, number of generations, elite size. In addition to the regular control parameters, the different algorithms need specific control parameters for their algorithm. A good tuning of different algorithm parameters is an integral factor that affects the efficacy of the algorithm. The inappropriate tuning of the algorithm parameters increases the computational effort or adheres to local limits. Teaching Learning-based Optimization (TLBO) algorithms are algorithms that need no algorithm-specific parameter. Jaya is a kind of TLBO algorithm but has only one-step and is user-friendly. The main aims of this paper are to present the Jaya algorithm and its application to the most prominent engineering problem. The validation and monitoring of existing technology in the presented Jaya include case studies, ranging from the recent CEC 2016 workbench to the common engineering challenges for the gear train, welded beam, three-bar truss system. The results achieved reflect the significance of the algorithms in comparison with most popular modern algorithms.