Research Article
Hybrid Differential Evolution-Particle Swarm Optimization Algorithm for Multiobjective Urban Transit Network Design Problem with Homogeneous Buses
Algorithm 2
Hybrid DE-PSO for multiobjective UTNDP.
| (1) | Initialize the swarm using DE (Algorithm 1) | | (2) | for | | (3) | fitness evaluation using passenger assignment | | (4) | | | (5) | Set | | (6) | end for | | (7) | for | | (8) | for | | (9) | set current particle = 1st particle in swarm | | (10) | select a particle randomly (except the selected 1st particle) in the swarm | | (11) | apply particle modification scheme to generate a modified particle (repair if infeasible) | | (12) | fitness evaluation using passenger assignment on the modified particle | | (13) | if modified particle is better than personal best | | (14) | update personal best and its fitness | | (15) | else if modified particle is better than global best | | (16) | update global best and its fitness | | (17) | end if | | (18) | end for | | (19) | new_population | | (20) | end for | | (21) | return Best |
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