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A biologically-inspired intelligent controller based on a computational model of emotional learning in mammal's brain is employed for flocking control of Multi-Agent Systems (MAS). The methodology, known as Brain Emotional Learning Based Intelligent Controller (BELBIC), is implemented in this application for the first time, enhancing the flocking strategy with multi-objective properties. The learning capabilities added by BELBIC to the flocking are very useful, especially when dealing with noises and/or system uncertainty. Furthermore, the low computational complexity of the proposed method makes it very promising for implementation in real-time applications. Numerical results of the BELBIC-based flocking for MAS demonstrate the effectiveness of the proposed approach.