Using a co-operative co-evolutionary genetic algorithm to solve a three-dimensional container loading problem

The paper presents the use of a co-operative co-evolutionary genetic algorithm (CCGA) in conjunction with a heuristic rule for solving a 3D container loading or bin packing problem. Unlike previous works which concentrate on using either a heuristic rule or an optimisation technique to find an optimal sequence of the packages which must be loaded into the containers, the proposed heuristic rule is used to partition the entire loading sequence into a number of shorter sequences. Each partitioned sequence is then represented by a species member in the CCGA search. The simulation results indicate that the use of the heuristic rule and the CCGA is proven to be highly efficient in terms of the minimal number of containers required in comparison to the results given by a standard genetic algorithm search. In addition, this helps to confirm that the CCGA is also suitable for use in a sequence-based optimisation problem.

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