This chapter applies the Bees Algorithm to optimising robot control systems, specifically focusing on serial manipulators. Serial manipulators are robotic arms with multiple joints and links. They are commonly used in industrial automation. The chapter explains the challenges of controlling these manipulators due to their nonlinear dynamics and the need for precise trajectory tracking. The first application discussed in this chapter concerns the identification of the inverse kinematics of a three-link manipulator. The chapter introduces the Multi-Layer Perceptron (MLP) as a neural network architecture to model the robot’s behaviour. It describes the MLP’s structure, consisting of input, hidden, and output layers, and the backpropagation algorithm used to train the network. The Bees Algorithm is employed to optimise the MLP’s weights and biases to minimise the error between the desired and actual robot trajectories. Compared to the standard gradient-based backpropagation algorithm, and an evolutionary optimiser, the Bees Algorithm achieved highly competitive results in terms of accuracy of the trained MLP model. The chapter then presents a second application concerning the optimisation of the parameters of a proportional-integral derivative (PID) control system for a one-link flexible arm manipulator. The experimental results demonstrated that the Bees Algorithm effectively optimises the PID controller, outperforming a manually optimised controller. The chapter concludes by highlighting the potential of the Bees Algorithm for optimising complex robot control systems and its applicability to various robotic applications.