Optimizing High-Dimensional Functions with an Efficient Particle Swarm Optimization Algorithm

Table 3

The performance of PSOTS, the traditional PSO algorithm (PSO), the AsynLnPSO algorithm (APSO), the LinWPSO algorithm (LPSO), GPSO, CCAS, and VPSO on 9 functions in the benchmark dataset when the dimensionality is 200.

Function		Ackley	Cigar	Griewank	Rastrigin	Rosenbrock	Noncon-rast	Schwefel	Tablet	Ellipse
PSOTS	Best	0.431	8313.319	0.006	125.642	266.531	142.388	135.922	1.145	213.868
	Mean	0.510	10573.300	0.009	190.698	291.663	180.543	148.872	1.749	426.983
	STD	0.059	1297.349	0.001	44.402	15.715	28.069	8.772	0.394	157.209
PSO	Best	0.623	13592.310	0.011	828.157	330.324	1162.641	129.083	1.924	442.334
	Mean	0.733	15953.110	0.013	908.032	360.406	1258.925	137.839	2.415	567.524
	STD	0.095	2141.955	0.001	65.745	25.679	57.524	6.567	0.290	87.083
APSO	Best	0.628	13927.090	0.012	761.458	333.122	1168.319	125.760	2.115	374.350
	Mean	0.728	16153.560	0.016	911.018	363.264	1284.167	137.659	2.590	615.184
	STD	0.063	1431.241	0.002	69.228	22.187	51.557	8.961	0.287	153.836
LPSO	Best	0.540	11784.830	0.010	766.147	287.388	1230.984	119.961	2.078	411.546
	Mean	0.651	13929.390	0.012	880.708	337.612	1375.003	130.168	2.392	548.331
	STD	0.055	2044.389	0.002	73.132	25.340	110.808	8.568	0.212	92.605
GPSO	Best	0.502	10276.490	0.011	513.656	345.566	922.450	142.323	1.765	320.551
	Mean	0.620	13015.940	0.017	746.526	432.668	1113.325	166.486	2.458	549.419
	STD	0.087	1986.996	0.002	109.377	20.7677	101.249	11.501	0.283	108.289
CCAS	Best	0.483	9755.112	0.010	523.218	302.989	782.621	126.101	1.574	386.631
	Mean	0.627	12079.051	0.013	841.210	422.458	944.007	144.237	2.459	523.554
	STD	0.114	1665.608	0.002	206.596	26.121	120.584	13.762	0.292	102.498
VPSO	Best	0.464	9264.162	0.010	353.963	298.692	541.078	102.796	1.436	370.183
	Mean	0.552	12345.613	0.017	641.174	347.596	829.347	138.759	2.130	502.851
	STD	0.045	1925.664	0.003	156.169	34.864	138.683	10.812	0.246	97.771