Computational Intelligence - February 2017 - 37
studied, the general principles of this study
The three test networks are all generated based on the
can provide useful reference to the realistic
TA planning. The three test networks are all
principles which can reflect the true nature of realistic
generated based on the principles which can
TA planning instances.
reflect the true nature of realistic TA planning
instances. Fig. 4 shows the network with a
total of 25 cells (network 1), and the networks with 30 cells (network 2) and 81 cells (network 3) have
A. The Parameters of the Networks
a similar structure to network 1. The numerical experiments
In our model, the road traffic density distributions of the three
conducted mainly have two goals:
networks are known in advance. We generate the traffic flow of
each road according to its type by sampling from uniform dis❏ Identify the effectiveness and rationality of the proposed
tributions for the three networks to simulate the real situation.
multi-objective TA planning model.
The cells crossed by each road and the limits of the traffic dis❏ Show the effectiveness of the M2M-based EMO algorithm
tributions for each type of roads are shown in Tables 1, 2 and 3.
to solve the proposed model.
The other control parameters are defined as follows:
For the first goal, a single-objective model proposed by
Subrata and Zomaya [8] is used as a comparison. The same
❏ The paging capacity of each cell: P BS = 28/s .
assumptions and parameters of the three networks are used to
❏ The telephone traffic load capacity of each MME:
ensure fair comparisons. The single-objective model established
P MME = 1500/s .
in [8] combines location update cost and paging cost to form a
❏ The total paging pi of cell i is generated from a Poisson dissingle-objective by the weighted sum method, where the cost
tribution with rate parameter m = 6 .
of a location update is considered to be ten times more than
❏ The population size is M = 100; the maximum number of
that of a paging. Three artificial intelligence techniques: SA, TS
function evaluations is 500 ) M for the 5 × 5 and 5 × 6
and EA are developed and applied in [8]. In our study, the EA
networks; maximum function evaluations is 1, 000 ) M for
is applied to their model and serves as a contrast. As is well
the 9 × 9 network.
known, the quality of solutions found by an EA can be highly
❏ The number of the subproblems is K = 10, and the size of
related to the number of iterations (function evaluations) that
each subpopulation is S = 10.
the algorithm uses. To be fair, the algorithms for the two models were both executed until the same maximum number of
B. Experimental Results and Analysis
function evaluations are reached. For the second goal, we comTen different groups of parameters for each network are genpare the results obtained by solving the proposed TA planning
erated for simulation to show the universality of the proposed
model using M2M to MOEA/D.
model. For direct comparison, the weighted sum of the
TABle 1 The road traffic density distributions
in the 5 × 5 network.
TABle 2 The road traffic density distributions
in the 5 × 6 network.
rOAd
TyPe
NuMBer
TrAFFic
FlOw
cellS crOSSed By
rOAdS
rOAd
TyPe
NuMBer
TrAFFic
FlOw
cellS crOSSed By
rOAdS
1. MAIn
rOAd
4
[200, 400]
{11, 12, 17, 22, 23, 24, 25}
1. MAIn
rOAd
4
[200, 400]
{3, 8, 13, 18, 22, 23, 24, 29, 30}
{21, 22, 18, 14, 15}
{26, 21, 16, 17, 18, 19, 20, 25}
{5, 10, 14, 19, 23, 28}
{5, 9, 13, 18, 23}
{3, 4, 10, 15}
{3, 8, 9, 10}
2. STreeT
7
[100, 250]
{2, 3, 4, 5}
2. STreeT
7
[100, 250]
{3, 8, 13, 18}
{3, 8, 12, 11}
{4, 8, 12}
{5, 10, 15}
{9, 10, 15, 20, 24}
{11, 12, 18, 23, 28}
{17, 13, 14, 10}
{6, 21, 16, 11}
{18, 19, 15}
{16, 17, 12}
{26, 27}
{19, 23}
3. Alley
4
[50, 150]
{8, 3, 4, 5}
{6, 11, 16, 21, 22}
3. Alley
4
[50, 150]
{8, 13, 19, 23, 28, 27}
{2, 7}
{1, 2, 3, 8, 9}
{8, 4, 5}
{7, 4, 5, 9}
{11, 12, 13}
{11, 12, 13}
FEbruary 2017 | IEEE ComputatIonal IntEllIgEnCE magazInE
37
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