A Novel Cooperation Multi-Objective Optimization Approach: Multi-Swarm Multi-Objective Evolutionary Algorithm Based on Decomposition (MSMOEA/D) - Liu, Rui; Chen, Hanning; Wang, Zhixue; Hu, Yabao (Frontiers in Energy Research, 2022)
In order to achieve good adaptability, medical bone implants for clinical applications need to have porous characteristics. From a biological and mechanical point of view, the design of porous structures requires both suitable porosity to facilitate cell ingrowth and suitable strength to avoid implant damage. To handle the multiobjective optimization problems of porous structure design, this work introduces an improved multi-objective optimization algorithm, which is called multi-swarm multi-objective evolutionary algorithm based on decomposition (MSMOEA/D), and the main idea is multi-swarm strategy. After a predetermined algebraic evolution, the whole swarm is evenly divided into several parts, and the elite non-dominated sorting mechanism is used to select the individuals with excellent performance and poor performance in the sub-swarms to exchange information between the sub-swarms. The performance of the MSMOEA/D algorithm is verified and validated on 12 constraint two-objective and three-objective benchmark functions, and compared with MOEA/D, MOEADM2M and MOEADDRA algorithms in terms of GD indicators. The solutions obtained by the proposed MSMOEA/D algorithm are accurate. Finally, the proposed MSMOEA/D algorithm is applied to optimize the constructed RS porous structure, and the porous optimized models with porosity of 50%, 60% and 70% are obtained.