[Retracted] A Classification Technique for English Teaching Resources and Merging Using Swarm Intelligence Algorithm

Algorithm 2

The enhanced PSO algorithm.

	Step 1: Determine the population number N, the weight coefficient ω, the Gaussian variance, the number of new particles, and the maximum number of iterations T before you begin to initialize the parameters of the particle swarm.
	Step 2: Calculate the fitness value of each individual particle in turn in accordance with the position of the particle, and after that, acquire the individual extreme value in addition to the global extreme value.
	Step 3: Calculate the position of the new particle as well as its fitness value, then compare the fitness of the new particle to the fitness of the particle with the lowest fitness in the population. Keep the particles with higher fitness and get rid of the ones with lower fitness.
	Step 4: When compared to the fitness value of the global extreme value in the current iteration, it is preferable to keep the point that has the highest fitness as the global extreme value. This is accomplished by performing the Gaussian mutation of the fireworks algorithm at the global extreme point, finding new position points around the global extreme point, and calculating their fitness.
	Step 5: Maintain the velocity and location of the particles.
	Step 6: Check to see if the algorithm has achieved the convergence criterion; if it has not, go back to Step 2 and try again.
	Step 7: At the end of the algorithm, the position with the most individual is output, that is, the optimal solution of the objective function.