.png){kind=icon}
.png){kind=icon}
Return
A multiobjective crayfish optimization algorithm for simultaneous topology, shape, and size optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s12008-025-02409-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
It is significantly challenging for design engineers to optimize a truss structure's topology, size, and shape. The improvement problem is then modeled as a multiobjective problem with objectives such as minimizing the structure's weight and maximizing reliability. This paper proposes a robust quality-based multiobjective crayfish optimization algorithm (MOCOA). Six different truss designs are used to test the proposed algorithm. It reveals that in the context of consistency and precision, MOCOA is a better algorithm when compared with recent algorithms, such as MOALO, MOBA, MODA, NSGA-II, DEMO, MOWCA, and MOEA-D, overall Friedman rank. Results are reported as superior in Pareto front, hypervolume, generational distance, and spacing metric. The research paper's finding indicates that MOCOA generates suitable Pareto-optimal solutions possessing strong convergence properties with excellent spread. These findings establish a robust foundation for forthcoming research on the optimization of truss structures.
Keywords:
Optimization
Reliability
Simultaneity
Topology
Truss structure
Journal
I
IF:
2.5
Papers:
159
Citations:
0
View more organizations