Modifying Particle Swarm Optimisation and Genetic Algorithm for Solving Multiple Container Packing Problems

Research works related to the computational intelligence algorithms (e.g. Particle Swarm Optimisation and Genetic Algorithm) and theirs applications have been extensively reported during the last few decades. In this work, we modified the classical Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) for solving multiple container packing problems by embedding two heuristics called the flexible arranging scheme and elitist strategy. Five instant datasets classified by the number of boxes to be packed into containers were used in the comprehensive computational experiments. The aim of this research was to benchmark the classical GA and PSO with the modification of GA and PSO in terms of quality of solution obtained and the execution time usage. From the experimental results demonstrated that the performance of MPSO outperformed other algorithms including GA, PSO and even MGA for all problem sizes. However, both PSO and MPSO required longer computational time than the GA-based methods especially for the large problem size.