A Many-Objective Optimization Algorithm Based on Weight Vector Adjustment

Algorithm 2

Evolutionary strategy.

	Input: Parent population Pt, Mutation rate F, Crossover rate CR
	Output: The new population Qt
(1)	For i = 1 : N
(2)	Random selection of particles
(3)
(4)	If rand() < p % Mutation operation
(5)	If rand() < 0.5
(6)
(7)	else
(8)
(9)	End If
(10)	else
(11)
(12)	End If
(13)	for j = 1 : V % Crossover operation
(14)	k = Random(1 : V)
(15)	If
(16)
(17)	else
(18)
(19)	End If
(20)	End for
(21)
(22)	End for
(23)