An Optimization Framework of Multiobjective Artificial Bee Colony Algorithm Based on the MOEA Framework

Table 2

The parameter settings of the seven multiobjective algorithms.

Algorithm	Parameter	Value	Remark
NSGA-II	sbx.rate	0.8	Crossover rate for simulated binary crossover
	sbx.distributionIndex	30.0	Distribution index for simulated binary crossover
	pm.rate	0.2	Mutation rate for polynomial mutation
	pm.distributionIndex	20.0	Distribution index for polynomial mutation
	withReplacement	True	Binary tournament selection
NSGA-III	Divisions	4	Number of divisions
	sbx.rate	0.8	Crossover rate for simulated binary crossover
	sbx.distributionIndex	30.0	Distribution index for simulated binary crossover
	pm.rate	0.2	Mutation rate for polynomial mutation
	pm.distributionIndex	20.0	Distribution index for polynomial mutation
ε-MOEA	sbx.rate	0.8	Crossover rate for simulated binary crossover
	sbx.distributionIndex	30.0	Distribution index for simulated binary crossover
	pm.rate	0.2	Mutation rate for polynomial mutation
	pm.distributionIndex	20.0	Distribution index for polynomial mutation
	Epsilon	0.1	ε values used by the ε-dominance archive
SMPSO	pm.rate	1.0/L	Polynomial mutation.
			L = individual length
	pm.distributionIndex	20.0	Distribution index for polynomial mutation
MOEA/D	de.crossoverRate	0.1	Crossover rate for differential evolution
	de.stepSize	0.5	Size of each step for differential evolution
	pm.rate	1.0/L	Mutation rate for polynomial mutation, L = individual length
	pm.distributionIndex	20.0	Distribution index for polynomial mutation
	neighborhoodSize	0.1	Neighborhood size used for mating
	Delta	0.9	Probability of mating
	eta	0.01	Maximum number of spots that an offspring can replace
GDE3	de.crossoverRate	0.1	Crossover rate for differential evolution
	de.stepSize	0.5	Size of each step for differential evolution
RMOABC	Adaptive grid number	25
	Limit	0.25∗100∗D	D denotes the dimension of decision variables