Research Article
A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems
Algorithm 1
Density-based velocity update (S, A, medSDE).
| (1) | sort the extra archive A in ascending order based on convergence value as in equation (5) | | (2) | for each solution | | (3) | identify its T neighborhood index as B(i) | | (4) | end for | | (5) | for each particle | | (6) | associate xi to the angular-guided closest particle with index j in A | | (7) | get the T nearest neighbors in A of y by the neighborhood index B(j) | | (8) | sort the T neighbors of y in an ascending order based on convergence value by equation (5) | | (9) | select the first angle-distance-based neighboring particle of y as lbesti | | (10) | select randomly from the top 20% particles in A as gbesti | | (11) | update the velocity of xi by equation (4) | | (12) | update the position by equation (3) | | (13) | evaluate the objective values for xi | | (14) | end for | | (15) | return S |
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