Modified Shuffled Frog Leaping Algorithm for Improved Pareto-Set Computation: Application to Product Transport in Pipeline Networks


An efficiency improvement of product transport through pipeline networks can be obtained by a better allocation of available resources. That is a hard combinatorial problem with multi-objective optimization characteristics due to different types of products to be moved and the high occupance rate of the network. This paper presents a novel modified shuffled frog leaping algorithm, named modified shuffled frog leaping Pareto approach (MSFLPA), to solve this resource allocation problem. This new algorithm combines the use of a small population and an archiving strategy with a procedure to restart the population using two auxiliary memories to store nondominated solutions (Pareto set) found during population evolution. To validate the performance and efficiency of the proposed MSFLPA in spread Pareto front, five Zitzler-Deb-Thiele functions are examined and compared against two well-known multi-objective genetic algorithms: NSGA-II and SPEA2. The numerical experiments indicate that MSFLPA yields spread solutions and converges to the true Pareto front, and it is verified to be efficient and competitive for solving multi-objective problem. After this validation, the MSFLPA is used to optimize the allocation of resources and to solve the scheduling problem of a real-world pipeline network and if compared with NSGA-II and mu GA, MSFLPA is verified to be a new effective alternative for solving multi-objective scheduling problems with more than two objectives as it is the case of the pipeline scheduling problems.