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Article Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents a variation on the original ABC algorithm, namely the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. In this work, CABC algorithm is used for optimizing six widely-used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO) and its cooperative version (CPSO) have been studied. The simulation results showed that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness and convergence speed.