Showing 3 results for Self-Adaptive Algorithm
M. Paknahad, P. Hosseini, A. Kaveh,
Volume 13, Issue 1 (1-2023)
Abstract
Optimization methods are essential in today's world. Several types of optimization methods exist, and deterministic methods cannot solve some problems, so approximate optimization methods are used. The use of approximate optimization methods is therefore widespread. One of the metaheuristic algorithms for optimization, the EVPS algorithm has been successfully applied to engineering problems, particularly structural engineering problems. As this algorithm requires experimental parameters, this research presents a method for determining these parameters for each problem and a self-adaptive algorithm called the SA-EVPS algorithm. In this study, the SA-EVPS algorithm is compared with the EVPS algorithm using the 72-bar spatial truss structure and three classical benchmarked functions
M. Paknahad, P. Hosseini, S.j.s. Hakim,
Volume 13, Issue 2 (4-2023)
Abstract
Metaheuristic algorithms have become increasingly popular in recent years as a method for determining the optimal design of structures. Nowadays, approximate optimization methods are widely used. This study utilized the Self Adaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm as an approximate optimization method, since the EVPS algorithm requires experimental parameters. As a well-known and large-scale structure, the 582-bar spatial truss structure was analyzed using the finite element method, and optimization processes were implemented using MATLAB. In order to obtain weight optimization, the self-adaptive enhanced vibration particle system (SA-EVPS) is compared with the EVPS algorithm.
M. Paknahad, P. Hosseini, A. Kaveh,
Volume 15, Issue 3 (8-2025)
Abstract
This study presents the application of the Self-Adaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm for large-scale dome truss optimization under frequency constraints. SA-EVPS incorporates self-adaptive parameter control, memory-based learning mechanisms, and statistical regeneration strategies to overcome limitations of traditional metaheuristic algorithms in structural optimization. The algorithm's performance is evaluated on three benchmark dome structures: (1) a 600-bar single-layer dome with 25 design variable groups, (2) an 1180-bar single-layer dome with 59 design variable groups, and (3) a 1410-bar double-layer dome with 47 design variable groups, all subject to natural frequency constraints. Comparative analysis against five state-of-the-art algorithms—Dynamic Particle Swarm Optimization (DPSO), Colliding Bodies Optimization (CBO), Enhanced Colliding Bodies Optimization (ECBO), Vibrating Particles System (VPS), and Enhanced Vibrating Particles System (EVPS)—demonstrates SA-EVPS's superior convergence characteristics and solution quality. Results show that SA-EVPS consistently achieves the lowest structural weights with remarkable stability across all test cases. The algorithm's self-adaptive mechanisms eliminate manual parameter tuning while the statistical regeneration mechanism prevents premature convergence in large-scale optimization problems. This research establishes SA-EVPS as a robust and efficient metaheuristic for frequency-constrained structural optimization of complex dome structures.
