Bat Algorithm is among the most popular meta-heuristic algorithms for optimization. Traditional bat algorithm work on sequential approach which is not scalable for optimization problems involving large search space, huge fitness computation and having large number of dimensions E.g. stock market strategies therefore parallelizing meta-heuristics to run on parallel machines to reduce runtime is required. In this paper, we propose two parallel variants of Bat Algorithm (BA) using MapReduce parallel programming model proposed by Google and have used these two variants for solving the Software development effort optimization problem. The experiment is conducted using Apache Hadoop implementation of MapReduce on a cluster of 6 machines. These variants can be used to solve various complex optimization problems by simply adding more hardware resources to the cluster and without changing the proposed variant code.