Size/Layout Optimization of Truss Structures Using Vibrating Particles System Meta-heuristic Algorithm and its Improved Version

Kaveh, A.; Khosravian, Masoud

doi:10.3311/ppci.18670

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Azizi et al 76 optimized the design of engineering problems using the Atomic Orbital Search algorithm. Kaveh and Khosravian 77 used Vibrating Particles System algorithm to optimize truss structures’ layout and size. Gandomi et al 78 utilized the Cuckoo Search algorithm for five truss design optimization problems.…”

Section: Related Workmentioning

confidence: 99%

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Azizi

Shishehgarkhaneh

Basiri

et al. 2023

Sci Rep

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In this paper, Squid Game Optimizer (SGO) is proposed as a novel metaheuristic algorithm inspired by the primary rules of a traditional Korean game. Squid game is a multiplayer game with two primary objectives: attackers aim to complete their goal while teams try to eliminate each other, and it is usually played on large, open fields with no set guidelines for size and dimensions. The playfield for this game is often shaped like a squid and, according to historical context, appears to be around half the size of a standard basketball court. The mathematical model of this algorithm is developed based on a population of solution candidates with a random initialization process in the first stage. The solution candidates are divided into two groups of offensive and defensive players while the offensive player goes among the defensive players to start a fight which is modeled through a random movement toward the defensive players. By considering the winning states of the players of both sides which is calculated based on the objective function, the position updating process is conducted and the new position vectors are produced. To evaluate the effectiveness of the proposed SGO algorithm, 25 unconstrained mathematical test functions with 100 dimensions are used, alongside six other commonly used metaheuristics for comparison. 100 independent optimization runs are conducted for both SGO and the other algorithms with a pre-determined stopping condition to ensure statistical significance of the results. Statistical metrics such as mean, standard deviation, and mean of required objective function evaluations are calculated. To provide a more comprehensive analysis, four prominent statistical tests including the Kolmogorov–Smirnov, Mann–Whitney, and Kruskal–Wallis tests are used. Meanwhile, the ability of the suggested SGOA is assessed through the cutting-edge real-world problems on the newest CEC like CEC 2020, while the SGO demonstrate outstanding performance in dealing with these complex optimization problems. The overall assessment of the SGO indicates that the proposed algorithm can provide competitive and remarkable outcomes in both benchmark and real-world problems.

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Section: Related Workmentioning

confidence: 99%

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Azizi

Shishehgarkhaneh

Basiri

et al. 2023

Sci Rep

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“…In order to make the development of objective function lucid, the length l is assumed to be unity (1 ... (7) ... (8) ... (9) ... (10) The next type of constraints has their origin in the overall stability of the truss. However, this needs to be considered for the members that are in compression.…”

Section: The Objective Functionmentioning

confidence: 99%

Optimal Design of Steel Planar Trusses Using Ant Lion Algorithm

Jayaram

2022

jmmf

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This paper elaborates on optimized design of steel structures directed towards the sustainability of materials. The case in point is steel trusses that are extensively used structural components. Though copious research is available on use of conventional optimization methods, nature-inspired optimization algorithms have received scarce attention particularly in optimal design of planar trusses. In this paper, the development of Ant Lion algorithm for the optimal design models for steel trusses is elaborated. A comprehensive comparison with the optimized sectional weights obtained by other nature inspired optimization algorithms implemented in earlier research by the author. They include elitism based genetic algorithm (EBGA), ant colony optimization (ACO), artificial honeybee optimization (AHBO), and Particle swarm optimization (PSO) algorithm. Four steel trusses with different articulations have been considered for this purpose. It is found that the optimal weights obtained by Ant Lion algorithm are almost on par with those obtained by PSO. The other three algorithms vary marginally. However, the convergence to overall weight of trusses is different for different algorithms. ALO took 100-200 iterations for the convergence. In fact, the convergence to optimized weights are faster in case of ALO and PSO in relation to other algorithms.

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Combined Size and Shape Optimization of Truss Structures Using the Heap-Based Optimizer (HBO) Algorithm

Bodalal

2024

Arab J Sci Eng

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Size/Layout Optimization of Truss Structures Using Vibrating Particles System Meta-heuristic Algorithm and its Improved Version

Cited by 3 publications

References 31 publications

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Optimal Design of Steel Planar Trusses Using Ant Lion Algorithm

Combined Size and Shape Optimization of Truss Structures Using the Heap-Based Optimizer (HBO) Algorithm

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