A Multiobjective Crayfish Optimization Algorithm for Simultaneous Topology, Shape, and Size Optimization

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.