A Hybrid Soft Computing Approach for Subset Problems

Table 6

Experimental results of SPP benchmarks using ACS and ACS + CP.

(a) ACS experimental results

Problem	ACS	RPD	RPI	MIC	Secs
sppaa01	94720	67.93	98.26	0.16	600.8
sppaa02	57632	88.99	97.72	0.36	270.4
sppaa03	93304	87.93	97.75	0.13	780.5
sppaa05	91134	63.21	98.38	0.14	712.3
sppaa06	54964	103.27	97.35	0.38	254.9
sppnw06	9788	25.33	99.35	1.26	78.7
sppnw08	n.f.	n.f.	n.f.	n.f.	n.f.
sppnw09	n.f.	n.f.	n.f.	n.f.	n.f.
sppnw10	n.f.	n.f.	n.f.	n.f.	n.f.
sppnw12	16060	13.76	99.65	8.1	12.3
sppnw15	67746	0	100	17.54	5.7
sppnw18	365398	7.42	99.81	0.83	120.7
sppnw19	12350	13.32	99.66	1.35	73.7
sppnw23	14604	16.52	99.58	17.47	5.7
sppnw26	6956	2.35	99.94	12.81	7.8
sppnw32	14886	0.06	100	8.06	12.4
sppnw34	11289	7.64	99.8	14.68	6.8
sppnw39	10758	6.73	99.83	19.2	5.2
sppnw41	11307	0	100	16.67	6
avg		31.53	99.2	7.45

(b) ACS + CP experimental results

Problem	ACS + CP	RPD	RPI	MIC	Secs
sppaa01	88435	50.41	98.71	0.14	707.1
sppaa02	52211	71.22	98.17	0.32	309.4
sppaa03	81177	63.5	98.37	0.16	600.5
sppaa05	84362	51.08	98.69	0.17	590.7
sppaa06	48703	80.11	97.95	0.33	300.9
sppnw06	8038	2.92	99.93	3.3	30.3
sppnw08	36682	2.2	99.94	0.32	309.7
sppnw09	69332	2.32	99.94	2.45	40.8
sppnw10	n.f.	n.f.	n.f.	n.f.	n.f.
sppnw12	14252	0.95	99.98	9.34	10.7
sppnw15	67743	0	100	16.13	6.2
sppnw18	345130	1.46	99.96	1.47	67.8
sppnw19	11858	8.81	99.77	1.42	70.1
sppnw23	12880	2.76	99.93	16.66	6
sppnw26	6880	1.24	99.97	15.87	6.3
sppnw32	14877	0	100	9.8	10.2
sppnw34	10797	2.95	99.92	17.53	5.7
sppnw39	10545	4.61	99.88	15.85	6.3
sppnw41	11307	0	100	19.23	5.2
avg		19.25	99.5	7.25