Abstract
This paper presents an efficient evolutionary algorithm for solving multiobjective
transportation problem MOTP. a new chromosome's structure was introduced, which is
adopted as it is capable to representing all possible feasible solutions. Also, in order to keep
the feasibility of the chromosome, a criterion of the feasibility was designed. Based on this
criterion the crossover and mutation were implemented and they can always generate feasible
chromosomes. To avoid an overwhelming number of solutions the algorithm maintains a
finite-sized archive of non-dominated solutions, which gets iteratively updated in the presence
of new solutions based on the concept of Epsilon-dominance. Epsilon dominance process
saves the most representative solutions. Finally, we report numerical results in order to
establish the actual computational burden of the proposed algorithm and to assess its
performances with respect to classical approaches for solving MOTP.