Uniformity-Comprehensive Multiobjective Optimization Evolutionary Algorithm Based on Machine Learning

Algorithm 4

Framework of MOEA-UCML.

	Input: N (population size), Q (targeted questions)
	Output: (final population).
(1)	(, M) ⟵ Parameter Settings (Q)
(2)	⟵ Initialize Variables (N, , M, gen1)
(3)	⟵ Non Domination Sort (, , M)
(4)	for  ⟵ gen1 to gen2 do
(5)	[, ] ⟵ Train and Measure SOM ()
(6)	Parent ⟵ Tournament Selection ()/ Select suitable sires for breeding /
(7)	′ ⟵ Genetic Operator (Parent, , M)/ Cross mutation to produce offspring /
(8)	⟵ Non Domination Sort (′, , M)
(9)	⟵ Replace Chromosome (, , M)/ Select individuals with frontier priorities /
(10)	IGD ⟵ Calculating the IGD value of the current frontier
(11)	if IGD > Limited Values then
(12)	break;
	end if
	end for
(13)	for  ⟵ gen2 to do
(14)	[, ] ⟵ Train And Measure SOM ()
(15)	Parent ⟵ Tournament Selection ()
(16)	⟵ Mutation NN (Parent, , M)/neuro-clustered optimization strategy /
(17)	⟵ Non Domination Sort (, , M)
(18)	⟵ Replace Chromosome (, , M)/ Select individuals with frontier priorities /
	end for
(19)	return