A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems

Table 4

The result of 2-dimensional (weight and volume versus value) knapsack problems.

Problem	D	Target weight ∣ volume	Total weight	Total volume	Total values	Optimal result	Algorithm	Outcomes				Runtime
								Worst	Mean	Best	Std
KP9	20	20∣25	111.63	170.27	3813.6	1197.94	HS	1197.94	1197.94	1197.94	0	7.90831
							NGHS	1197.94	1197.94	1197.94	0	8.092175
							EHS	1197.94	1197.94	1197.94	0	8.978277
							ITHS	1197.94	1197.94	1197.94	0	8.588568
							HSTL	1197.94	1197.94	1197.94	0	8.31578
KP10	50	114∣133	400.56	408.57	10211	5002.81	HS	4849.19	4935.062	5002.81	45.16856	19.03619
							NGHS	4917.42	4963.421	5002.81	32.60289	19.15758
							EHS	4933.24	4983.169	4998.27	24.18182	23.48809
							ITHS	4926.07	4975.32	5002.81	29.72861	20.82369
							HSTL	4995.48	5000.063	5002.81	2.981432	18.9528
KP11	100	1500∣3000	3090.5	4599.2	15278	11480.84	HS	11393.59	11438.024	11477.52	35.010842	51.07192
							NGHS	11440.36	11457.378	11480.84	16.483244	51.2287
							EHS	11456.44	11468.79	11480.84	10.221502	65.89753
							ITHS	11431.96	11452.068	11480.84	18.277327	54.87239
							HSTL	11466.63	11477.33	11480.84	6.153989	45.31429
KP12	1000	30000∣40000	250320	400090	157890	unknown	HS	38064.94	39108.266	39924.89	866.36169	414.50957
							NGHS	41709.51	42048.386	42280.62	235.6772	424.71994
							EHS	40627.36	40766.678	40903.3	123.45544	454.46526
							ITHS	42485.83	42671.15	42928.94	163.78948	439.51866
							HSTL	43953.97	44119.987	44409.66	119.80076	393.5962
KP13	300	800∣700	13605	14157	48464	unknown	HS	7070.4	7140.222	7254.76	73.854458	36.347165
							NGHS	7040.92	7153.61	7249.1	81.266043	36.41226
							EHS	7253.67	7299.32	7369.58	48.490527	39.440654
							ITHS	7150.24	7202.6	7244.01	42.540662	39.450648
							HSTL	7345.46	7367.95	7390.52	18.42674	29.889155

Bold indicates best results.