Base Station Placement in WLANs - Multiobjective Optimization Methodology: A Jumping Gene Approach - page 187

Table 8.4
Means.and.Standard.Deviations.of.the.Deb.and.Jain.Convergence.Metric.
for Scenarios.a.(90.dB).and.b.(80.dB)

Scenario MOGA NPGA2 NSGA2 SPEA2 PAES MICROGA JG

a:.90dB 0.10833 0.09613 0.08099 0.07837 0.13906 0.29506 0.07679

(0.05951) (0.04144) (0.02254) (0.04616) (0.06530) (0.03673) (0.01814)

b:.80dB 0.12878 0.13489 0.10600 0.11018 0.14194 0.43459 0.10277

(0.03607) (0.04097) (0.02646) (0.03973) (0.06876) (0.05750) (0.02709)

Note: . The.best.result.for.each.scenario.is.marked.in.bold,.and.the.values.in.parentheses.represent.

the.standard.deviation.

Table 8.5
Means.and.Standard.Deviations.of.the.Spread.for.Scenarios.a.(90.dB).and.b.(80.dB)

Scenario MOGA NPGA2 NSGA2 SPEA2 PAES MICROGA JG

a:.90dB 0.98971 0.99518 0.97865 1.00420 1.00764 0.98532 0.97854

(0.01251) (0.01001) (0.01751) (0.02027) (0.01095) (0.01735) (0.02118)

b:.80dB 0.96055 0.97978 0.94266 1.00016 1.03264 0.98283 0.92105

(0.05362) (0.04832) (0.07118) (0.06209) (0.03862) (0.04355) (0.07829)

Note: . The.best.result.for.each.scenario.is.marked.in.bold,.and.the.values.in.parentheses.repre-
sent.the.standard.deviation.

90

80

70

60

50

40

30

20

10

0

NPGA2 PAES MICROGA MOGA SPEA2 NSGA2 JG

Algorithm

Figure 8.4
Total. number. of. extreme. nondominated. solutions. for. scenario. a:. 90. dB.. (From. Chan,. T.. M.,.
Man,.K..F.,.Tang,.K..S.,.Kwong,.S.,.A.jumping-genes.paradigm.for.optimizing.factory.WLAN.
network, IEEETransactionsonIndustrialInformatics ,.3(1),.33-43,.2007.)