Research Article

Potential Odor Intensity Grid Based UAV Path Planning Algorithm with Particle Swarm Optimization Approach

Table 6

Results of the paired samples -test.
(a) -test of cost values

Paired differencesdfSig.
(2-tailed)
MeanStd. deviationStd. error mean95% confidence interval of the difference
LowerUpper

Pair 1 proposed method: PSO−.05496.04522.01011−.07612−.03379−5.43519.000
Pair 2 proposed method: FA−.42184.04838.01082−.44448−.39919−38.99619.000
Pair 3 proposed method: GA−.06850.02255.00504−.07905−.05794−13.58219.000

(b) -test of planned path lengths

Paired differencesdfSig.
(2-tailed)
MeanStd. deviationStd. error mean95% confidence interval of the difference
LowerUpper

Pair 1 proposed method: PSO−13.548559.279052.07486−17.89128−9.20582−6.53019.000
Pair 2 proposed method: FA−34.3647719.563674.37457−43.52085−25.20869−7.85619.000
Pair 3 proposed method: GA−18.120884.41683.98763−20.18802−16.05374−18.34819.000