Population-based metaheuristic algorithms have been used to solve challenging optimization problems, and numerous modifications to make these algorithms more efficient have been proposed. Jaya is one of these algorithms that has been modified and utilized to solve many hard optimization problems. One such modification to the original algorithm is the Modified Jaya (MJAYA) algorithm, which, like Jaya, uses information about the best and worst candidate solutions to determine the population’s search direction, thereby limiting and focusing the search. Recent research has demonstrated that the MJAYA algorithm is not effective at solving systems of nonlinear equations transformed into equivalent nonlinear optimization problems. Systems of nonlinear equations are probably the most difficult class of numerical mathematics problems to solve, which justifies the use of metaheuristic approaches in their resolution. This paper proposes a new population-based optimization algorithm, the Improved Modified Jaya (IM-Jaya) algorithm, which, despite being a generic metaheuristic optimization algorithm, is here used to solve systems of nonlinear equations. The proposed algorithm strikes a better balance between global exploration and local exploitation, allowing for a broader and more exhaustive exploration of the search space as well as a more efficient local search in its most promising regions.