The Jaya optimization algorithm is widely recognized as one of the simpler and faster population-based heuristic search algorithms. However, it can suffer from convergence issues for complex real-world problems which are nonlinear and non-differentiable in nature. To deal with Jaya77 , this paper proposes to enhance the performance of the Jaya algorithm by considering a weight parameter in the search process. Two methods are proposed to vary the weight parameter, one is a linear weight (LW) and the other is a fuzzy logic based mechanism. Experimental results show that MJOA is quite accurate and is much faster in convergence than the original Jaya algorithm for both LW and fuzzy-based methods.

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