Mathematical Problems in Engineering

Research Article

An Improved Particle Swarm Optimization Algorithm Based on Centroid and Exponential Inertia Weight

Table 5

Comparison of experimental results of success rate and average convergence iteration.


Functions	Target precision	Indicators	BPSO	EPSO	CPSO	ACL-PSO	IPSO

	0.1	SR	0	0	0	86	100
	0.1	ACI	1000	1000	1000	901.28	676.66
	0.6	SR	0	4	0	98	100
	0.6	ACI	1000	974.80	1000	747.80	607.82
	1	SR	0	26	4	100	100
	1	ACI	1000	818.98	989.02	439.76	472.48
	1.5	SR	16	100	64	100	100
	1.5	ACI	991.82	327	840.20	23.10	269.64

	10⁻⁷	SR	0	0	0	48	100
	10⁻⁷	ACI	1000	1000	1000	967.60	369.50
	20	SR	0	4	0	100	100
	20	ACI	1000	971.98	1000	40.92	262.20
	30	SR	0	8	2	100	100
	30	ACI	1000	945.64	998.82	42.42	267.02
	40	SR	2	30	12	100	100
	40	ACI	982.44	775.36	944.36	28.62	254.82

	19	SR	0	0	0	70	100
	19	ACI	1000	1000	1000	932.98	336.88
	30	SR	0	8	0	100	100
	30	ACI	1000	947.38	1000	444.52	275.40
	40	SR	0	14	2	100	100
	40	ACI	1000	903.22	983	333.14	608.60
	500	SR	6	62	30	100	100
	500	ACI	998.72	530.34	943.50	36.18	28.40

	10⁻¹⁰	SR	0	0	0	78	100
	10⁻¹⁰	ACI	1000	1000	1000	920.22	330.84
	0.01	SR	0	10	8	100	100
	0.01	ACI	1000	932.46	980.92	229.54	255.48
	0.05	SR	4	68	18	100	100
	0.05	ACI	998.68	504.24	959.46	53.96	225.48
	0.1	SR	14	90	52	100	100
	0.1	ACI	994	294.80	899.40	34.64	209.18