Table 5 summarizes the comparison of DGSO, GSO, and RDGSO with the different dimensions (D = 50, 100, 200), respectively.
Next we compare the convergence speed between the DGSO and the other four algorithms including GSO, OGSO, RGSO, and RDGSO.
For convenience, two algorithms DGSO that used the diversity-guided operator and OGSO that is the opposition-based algorithm without diversity-guided operator are compared.
The proposed DGSO is also compared with some other well-known evolutionary algorithms  based on these benchmark functions.
Table 7 describes the comparison results of DGSO and some other algorithms when D = 100.
In this paper, we proposed a diversity-guided group search optimizer (DGSO) that is realized based on an opposition-based learning method and a diversity-guided operator mechanism.
As shown in Table 4, the minimization cost of DGSO is only 11.425, while the best optimal cost reported in previous literatures is 11.471.
Figure 7 describes the process of optimization using GSO and DGSO with a size of 100 populations and 150 generations, respectively.
The cost of the proposed DGSO is also compared with the performance of some other algorithms; refer to Table 6.
This paper presents the DGSO and shows its application to mobile location management problem.
(1) We proposed the design of DGSO with the aid of diversity operator.
(2) We employ GSO and DGSO to deal with the mobile location management problem.