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This paper introduces a bio-inspired meta-heuristic soft computing method to optimize the performance of a PID controller used in DC motor speed control. APID controller is designed for an adopted second order DC motor system and the evolutionary algorithm based on Grey Wolf Optimizer is used to optimize the controller. In MATLAB/Simulink environment, the best set of PID parameters thus obtained from the optimization are used with a step input to the DC motor to get the transient response specifications such as rise time, settling time, maximum over shoot and steady state error. These results are then compared with results from other conventional techniques and soft computing methods to look for the most efficient method to get best transient response in aDC Motor.

This paper presents an algorithm called Grasshopper optimization for solving short-term hydrothermal scheduling problem. The objective of this problem is to reduce the generation cost by optimizing the output of power generation of different thermal and hydro plants for a certain time interval. A non-linear relationship between hydropower generation, net head and rate of water discharge is considered here. A complex piecewise output limit and headsensitive conversion of water-to-power is considered here. To investigate the performance of this new technique, three test systems have been considered. The results obtained by this Grasshopper optimization algorithm are compared with other well-known soft computing techniques. The efficacy of this proposed technique has been verified after comparison with other similar soft techniques.

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