Sizing and shape optimization of truss employing a hybrid constraint-handling technique and manta ray foraging optimization

This paper presents an efficient constraint-handling technique (CHT) for metaheuristic algorithms in the size and shape optimization of truss structures. During the search process, the proposed CHT utilizes an improved Deb rule to filter redundant structural analyses and maps the candidate designs onto the feasible boundary for structural optimization to improve its search ability and stability based on the mapping strategy. The performance of the newly developed Manta Ray Foraging Optimization (MRFO) algorithm using the proposed CHT in structural optimization was also examined. Five truss optimization problems are used to examine the efficiency of the hybrid CHT compared with the improved Deb rule, the EDP method, and the mapping strategy. Four widely used metaheuristic algorithms, including HS, PSO, TLBO, and CS, have also been used to evaluate the performance of the MRFO in structural optimization. Numerical results demonstrate that the hybrid CHT can markedly improve both the search capacity and computational efficiency of metaheuristic algorithms. The MRFO does not show obvious weakness compared with existing algorithms in structural optimization. A comparison analysis also shows that the performances of the hybrid CHT vary across optimization algorithms.