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

Table 5

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 500.

Function		Ackley	Cigar	Griewank	Rastrigin	Rosenbrock	Noncon-rast	Schwefel	Tablet	Ellipse
PSOTS	Best	0.492	25412.630	0.009	478.891	706.558	493.205	414.928	3.475	1496.384
	Mean	0.581	29691.450	0.012	553.646	803.327	557.334	435.652	4.947	2092.524
	STD	0.051	3513.855	0.002	70.573	51.248	50.482	10.066	1.145	320.057
PSO	Best	0.829	45422.870	0.019	999.299	1022.024	3829.275	423.314	5.766	2244.141
	Mean	0.886	56384.080	0.022	2780.208	1111.354	4051.348	433.986	7.550	3255.934
	STD	0.052	5545.354	0.002	916.323	47.555	167.972	7.313	0.963	199.579
APSO	Best	0.833	50024.720	0.019	1126.165	1000.456	3746.506	418.279	6.807	2937.273
	Mean	0.929	56943.480	0.023	3075.081	1084.608	4065.724	433.210	8.100	3627.655
	STD	0.049	4846.618	0.003	691.812	48.341	217.894	8.016	1.265	422.285
LPSO	Best	0.742	43541.120	0.017	920.020	943.407	3905.713	411.002	5.975	2373.675
	Mean	0.806	48230.900	0.020	2685.457	985.057	4112.780	422.941	7.051	3115.352
	STD	0.043	3195.987	0.002	933.487	44.286	135.469	10.654	0.603	443.890
GPSO	Best	0.697	28284.351	0.015	768.416	1048.064	924.820	441.750	4.485	1907.092
	Mean	0.775	38520.982	0.024	2072.027	1253.445	1278.837	503.868	7.090	3093.015
	STD	0.061	4961.206	0.002	740.987	36.754	112.2568	15.367	1.076	769.6676
CCAS	Best	0.602	26503.243	0.013	1169.143	852.805	890.7993	423.028	4.560	1809.055
	Mean	0.738	39164.056	0.020	2645.188	1270.997	1264.482	462.079	6.907	2947.759
	STD	0.076	4060.912	0.004	301.950	50.363	132.752	12.689	0.606	274.5918
VPSO	Best	0.599	27384.475	0.012	776.843	901.369	860.366	434.518	4.089	1731.14
	Mean	0.691	38449.613	0.022	1961.521	1062.914	1363.843	455.697	6.049	2571.514
	STD	0.038	4278.437	0.005	357.148	46.818	136.64	4.991	1.399	720.7601