Enhanced Memetic Algorithm-Based Extreme Learning Machine Model for Smart Grid Stability Prediction

Algorithm 3

	Let be the smart grid operational data, where is the instance of the smart grid operational feature value and is the status of the grid.
(1)	Randomly generate the population , where representing is an instance of the ELM parameter set.
(2)	Evaluate the fitness of each in . .
(3)	Select fittest in as .
(4)	Compute teaching factor and population mean.
(5)	Generate from in : , where is a random number.
(6)	Perform the updating by comparison of each in with the corresponding in .
(7)	For each pair of randomly chosen and in , improvise them by using the following equation:
(8)	Find the best solution in based on the highest fitness.
(9)	Generate a new solution from by using a simulated annealing process .
(10)	Update the population by replacing old with new (returned from the procedure ).
(11)	If a maximum generation is reached, then assign .
	Else, go to step 2.
(12)	Return .