A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

Table 1

Experimental parameter setting.

ABO		MIMM-ACO		IEO		MMAS		CGAS
Parameter	Value	Parameter	Value	Parameter	Value	Parameter	Value	Parameter	Value
Population	40	Ants (n)	10	Population		Population		Generation	100
	2.0	β	2.0	NITER	200000	β	5.0	β	2.0
lp1	0.6		0.1		3.0		0.99		0.1
lp2	0.5	α	1.0	α	Cost	α	1.0	Ro	0.33
N/A	1.0	Ǫ	200	B	Best	Φij	rand (−1, 1)	Crossover rate	1.0
N/A	N/A	N/A	N/A	N/A	Known cost	N/A
—	N/A	Qo	0.85	N/A	N/A	Qo	0.9	qo	0.9
N/A	N/A	Φ	1/n	N/A	N/A	Ǫ	200	ϕr	0.3
N/A	N/A	min	1.001ϕ	N/A	N/A	N/A	N/A		0.2
N/A	N/A	θ	1.5	N/A	N/A	N/A	N/A	τmin	τmax/20
N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	τmax	1 − ()
Total no of runs	50	—	50	—	50	—	50	—	50