@article{PEREZARACIL2021100958,

title = {Memetic coral reefs optimization algorithms for optimal geometrical design of submerged arches},

author = {Jorge Pérez-Aracil and C. Camacho-Gómez and A. M. Hernández-Díaz and E. Pereira and David Camacho and Sancho Salcedo-Sanz},

url = {https://www.sciencedirect.com/science/article/pii/S2210650221001206},

doi = {https://doi.org/10.1016/j.swevo.2021.100958},

issn = {2210-6502},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Swarm and Evolutionary Computation},

volume = {67},

pages = {100958},

abstract = {This paper deals with the geometrically nonlinear analysis of submerged arches by means of memetic Coral Reefs Optimization algorithms. The classic design of submerged arches is only focused on calculating the bending stress-less shape (funicular shape) of the structure. Nevertheless, recent works show that this funicular shape can be approached by using a parametric family curve, which also allows a multi-variable optimization of the arch’s geometry. Using this novel parametric set of curves, we propose a new Coral Reefs Optimization (CRO) algorithm based on a memetic approach to tackle the geometrically nonlinear design of submerged arches. Specifically, the proposed CRO approaches have been tested with different search procedures as exploration operators, and we also test a multi-method version of the algorithm, the Coral Reefs Optimization with Substrate Layers (CRO-SL), which considers several search procedures within the same evolutionary population. A local search to improve the solutions has been considered in all cases, to obtain powerful memetic operators for this problem. It is also shown how the different memetic versions of the CRO (specially those involving multi-methods and Differential Evolution search procedures), together with the parametric encoding, are able to obtain nearly-optimal geometries for underwater installations. The performance of the proposed algorithm has been compared with state-of-the-art algorithms for optimization: L-SHADE and HCLPSO. Statistical tests have carried out with the aim of comparing the results. It is shown that there is not significant differences between the proposed results by the three algorithms.},

keywords = {CRO-SL, Evolutionary Computation, Memetic Algorithms, Nonlinear analysis, Submerged arches},

pubstate = {published},

tppubtype = {article}

}