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The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. The goal of grid task scheduling is to achieve high system throughput and to allocate various computing resources to applications. Many different methods have been proposed to solve this problem. Some of these methods are based on heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper we introduce a new task scheduling algorithm based on Ant Colony Optimization (ACO). According to the experimental results, the proposed algorithm confidently demonstrates its competitiveness with previously proposed algorithms.