An Evolutionary Algorithm with Clustering-Based Assisted Selection Strategy for Multimodal Multiobjective Optimization

Algorithm 1

Algorithm 1: General framework.

Input: N (the number of solutions in population)
Output: P (final population)
(1)	initialize a population P
(2)	initialize weight vectors
(3)	while the stopping criterion is not met
(4)	P' = Crossover + Mutation (P)
(5)	U = P∪P′
(6)	(F1, …, Ft) = Non-dominated Sorting (U)
(7)	normalize U
(8)	(C1, …, CK) = DBSCAN (U, ε, MinPts)
(9)	P = Addition Operator//Algorithm 2
(10)	normalize P
(11)	() = Association Process (P, W)//Algorithm 3
(12)	P = Deletion Operator//Algorithm 4
(13)	end while
(14)	return P