multi-objective evolutionary algorithm

Trip distribution deals with estimation of trips distributed among origins and destinations and is one of the important stages in transportation planning. Since in the real world, trip distribution models often have more than one objective, multi-objective models are developed to cope with a set of conflict goals in this area. In a proposed method of adapted non-dominated sorting algorithm (ANSA) is introduced and applied on a multi objective trip distribution model. The objectives considered are: (1) maximization of the interactivity of the system, (2) minimization of the generalized costs and (3) minimization of the deviation from the observed year. in proposed ANSA using the sorting process of NSGA II and two proposed adapted operators a new adapted algorithm is introduced and applied to solve the three-objective model. To test the performance of the proposed algorithm, a set of Hong Kong data is used and results of applying proposed algorithm is compared to other models of the literature. The results show that proposed algorithms has better performance rather than the algorithms of the literature.