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Advances, Systems and Applications

Table 3 The settings of algorithm parameters QoS aggregation functions

From: A service composition method using improved hybrid teaching learning optimization algorithm in cloud manufacturing

Algorithm

Parameters

PSO

The inertia weight w varies from 0.9 to 0.7 linearly, the learning factors c1 varies from 2.5 to 0.5 linearly, while c2 varies from 0.5 to 2.5.

IGWO

The random vectors r1,r2 = rand[0, 1], the control coefficients c1 = 0.3 and θ = 2, while a varies from 2 to 0 linearly.

TLBO

The random vectors r, rj = rand[0, 1], the teaching factor TF is equal to 1 or 2.

Hybrid-TC

The random vectors r, rj, r1, r2 = rand[0, 1], and the random numbers c1, c2 = rand[− 1,1], the teaching factor TF is equal to 1 or 2.

Improved-TC

The random vectors r,rj, r1,r2 = rand[0, 1], and the random numbers c1, c2 = rand[−1,1], the teaching factor TF is equal to 1 or 2.