Uniformity-Comprehensive Multiobjective Optimization Evolutionary Algorithm Based on Machine Learning

Algorithm 1

Initialize variables.

	Input: N (population size), (number of decision variables), M (number of targets), gen1 (number of first stage iterations)
	Output: (phase 1 population).
(1)	⟵ Random (N, )
(2)	⟵ Non Domination Sort (, , M)
(3)	[Init Unevenness, ∼] ⟵ Train and Measure SOM ()/ Record initial distance /
(4)	for  ⟵ 2 to gen1 − 1 do
(5)	′ ⟵ Random (N, V)
(6)	⟵ Non Domination Sort (P ∪ P′, V, M)
(7)	⟵ Uniform extraction of individuals from each layer
(8)	end for
(9)	return