Showing 22 results for Meta-Heuristic Algorithm
M. Shahrouzi, M. Fahimi Farzam, J. Gholizadeh,
Volume 15, Issue 2 (4-2025)
Abstract
The tuned mass damper inerter systems have recently received considerable attention in the field of structural control. The present work offers a practical configuration of such a device, called double tuned mass damper inerter (DTMDI) that connects the inerter into the damper masses rather than be attached to the main structure. Soil-structure interaction is also taken into account for the soft and dense soils as well as for the fixed based condition. The H∞ norm of the transfer functions for the roof response is minimized as the objective function. The parameters of DTMDI are optimized using opposition-switching search as an efficient parameter-less algorithm in comparison with lightning attachment procedure optimization, sine cosine algorithm and particle swarm optimization. The system performance is evaluated in the frequency domain, as well as in the time domain under various earthquakes including far-field records, near-field records with forward directivity and with fling-step. The results show superiority of opposition-switching search for optimal design of the proposed DTMDI so that it can significantly reduce both the roof displacement and acceleration response for all the SSI conditions.
M. Shahrouzi, A.m. Taghavi,
Volume 15, Issue 3 (8-2025)
Abstract
Sound Energy Optimizer (SEO) is a recent metaheuristic algorithm inspired by the propagation and reception of sound waves in physical environments. While conventional metaheuristics that rely on random number generators with certain distributions, SEO can utilize various real-world or simulated sound signals as the source of stochasticity to guide its search process. Concerning structural design by SEO, the effect of natural sound signals is compared with the artificial signals generated from uniform or normal distributions. In this regard, a 244-bar power transmission tower and a 1016-bar double-layer grid are simultaneously optimized with continuous geometry as well as discrete sizing variables to evaluate the impact of input signals on convergence behavior, solution quality and robustness of the algorithm. A sensitivity analysis is conducted to calibrate key control parameters of SEO. The results declare that the nature of the input sound signal can significantly affect the algorithm’s exploration-exploitation balance. In this study, the "Knocking sound" signal yields the best performance, while the synthetic random signals revealed less stable optimization trajectories.
