An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints

Table 2

The performance experiment result 1.

Number of nodes	Parameter		General approach			AEA*
	𝑛	pc0	Best solution	Frequency of best	CPU Time (sec)	Best solution	Frequency of best	CPU time (sec)
7	7; 14	0.6; 0.7	26; 26	6; 3	0.922; 0.015	26; 26	1; 4	0.006; 0.016
25	25; 50	0.6; 0.7	134; 134	10; 5	10.422; 6.042	134; 134	4; 5	0.047; 0.094
35	30; 35; 70	0.6; 0.7; 0.8	177; 180; 180	11; 12; 11	18.803; 23.844; 19.424	177; 177; 177	9; 10; 16	0.218; 0.235; 0.625
45	45; 45; 50	0.6; 0.7; 0.8	214; 214; 209	15; 17; 18	44.314; 38.688; 42.124	209; 207; 207	18; 19; 17	1.047; 1.75; 1.547
70	70; 70; 140	0.6; 0.7; 0.8	383; 372; 372	33; 21; 27	532.406; 229.319; 245.247	363; 363; 363	43; 40; 45	9.016; 9.796; 14.281

∗The proposed adaptive evolutionary algorithm.