A challenging engineering optimization problem in electrical power generation is the unit commitment problem (UCP).Determining the scheduling for the economic consumption of production assets over a specific period is the premier objective of UCP.This paper presents a take on solving UCP with Binary Slime Mould Algorithm (BSMA).SMA is a recently usc trojans snapback hat created optimization method that draws inspiration from nature and mimics the vegetative growth of slime mould.
A binarized SMA with constraint handling is proposed and implemented to UCP to generate optimal scheduling for available power resources.To test BSMA as a UCP optimizer, IEEE standard generating systems ranging from 10 to 100 units along with IEEE 118-bus system are used, and the results are then compared with existing approaches.The comparison reveals the superiority of BSMA over sequal eclipse 5 battery all the classical and evolutionary approaches and most of the hybridized methods considered in this paper in terms of total cost and convergence characteristics.