Multi-objective structural profile optimization of ships based on improved Artificial Bee Colony Algorithm and structural component library

This paper addresses the multi-objective, design variables discrete problem of river boat profile structure optimization. The study considers not only the economic performance but also the stability of the boat to a certain extent when selecting the optimization objectives. First, the standard component library of the vessels is established and the discrete structural model is converted into a continuous optimization model based on the component library index. Second, to balance the global search and local search capability of the optimization algorithm, an adaptive Artificial Bee Colony Algorithm (I-ABC) is proposed. I-ABC uses a modified chaotic mapping to improve the diversity of nectar sources, and adaptive search and following mechanisms are adopted for the leading and following bee stages, respectively. Then, a Dominating Artificial Bee Colony Algorithm (D -ABC) is proposed for multi-objective optimization by introducing Pareto domination and considering the degree of individual crowding in terms of hypersphere neighborhood. Then, numerical simulations and comparative analysis of the proposed algorithm are performed using different test functions, and the results show that I-ABC and D-ABC have excellent performance in both single-objective and multi-objective optimization. Finally, using the component library established in this paper and the proposed improved Bee Colony Algorithm to optimize the profile structure of a chemical-liquid tanker, the optimization results show that the maximum reduction of the ship profile area is 5.08% and the maximum reduction of the vertical center height is 7.427%. The proposed optimization algorithm is also applicable to other river boats.

Automated summary

This paper proposes a multi-objective optimization approach for the discrete problem of river boat profile structure optimization. A component library is established to convert the discrete structural model into a continuous optimization model. An adaptive Artificial Bee Colony Algorithm (I-ABC) is then proposed to balance the global and local search capability. Additionally, a Dominating Artificial Bee Colony Algorithm (D-ABC) is introduced for multi-objective optimization by considering Pareto domination and individual crowding.