A Modified Jaya Algorithm for Mixed-Variable Optimization Problems

Abstract

<jats:title>Abstract</jats:title><jats:p>Mixed-variable optimization problems consist of the continuous, integer, and discrete variables generally used in various engineering optimization problems. These variables increase the computational cost and complexity of optimization problems due to the handling of variables. Moreover, there are few optimization algorithms that give a globally optimal solution for non-differential and non-convex objective functions. Initially, the Jaya algorithm has been developed for continuous variable optimization problems. In this paper, the Jaya algorithm is further extended for solving mixed-variable optimization problems. In the proposed algorithm, continuous variables remain in the continuous domain while continuous domains of discrete and integer variables are converted into discrete and integer domains applying bound constraint of the middle point of corresponding two consecutive values of discrete and integer variables. The effectiveness of the proposed algorithm is evaluated through examples of mixed-variable optimization problems taken from previous research works, and optimum solutions are validated with other mixed-variable optimization algorithms. The proposed algorithm is also applied to two-plane balancing of the unbalanced rigid threshing rotor, using the number of balance masses on plane 1 and plane 2. It is found that the proposed algorithm is computationally more efficient and easier to use than other mixed optimization techniques.</jats:p>

Dynamic balancing of cleaning unit used in agricultural thresher using Jaya algorithm

“…The convergence rates show that Jaya algorithm takes 93.6 per cent function evaluations compared to GA for all two cases. The algorithm is considered more efficient if it takes fewer function evaluations to find the optimum solution (Singh and Chaudhary, 2018b). Hence, Jaya algorithm is comparatively more efficient compared to GA.…”

Plant competition optimization: A novel metaheuristic algorithm

“…It is a simple algorithm with just a few parameters that are trapped into local optima easily. New versions of the JAYA algorithm such as chaotic JAYA (Farah & Belazi, 2018), self‐adaptive JAYA (Venkata Rao & Saroj, 2017), modified Jaya algorithm [MJAYA] (Elattar & ElSayed, 2019) and C‐JAYA (Singh & Chaudhary, 2018) tried to overcome its limitations and improve its performance. Sine‐cosine algorithm [SCA] (Mirjalili, 2016a) is certainly a mathematical model based on trigonometric sine and cosine functions, upon which numerous other algorithms have been developed and which have been used on a wide range of engineering domains, including shape optimization in the automobile industry (Yıldız, Pholdee, Bureerat, et al, 2020).…”