Shape and size optimization of trusses with multiple frequency constraints using harmony search and ray optimizer for enhancing the particle swarm optimization algorithm

Kaveh, A.; Javadi, Seyed Mohammad

doi:10.1007/s00707-013-1006-z

Cited by 69 publications

(22 citation statements)

References 18 publications

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“…However, there is a slight discrepancy in the attached mass (m), Young's modulus (E) and the specific mass (q) values employed by different authors. Hence, we analyze here three cases with different values of m, E and q to fairly compare the results of the SGA to the best ones available in the literature [39,40]. The parameters of each one of these cases are detailed in Table 2.…”

Section: Design Variablesmentioning

confidence: 98%

“…y coordinates (m) and areas (cm 2 ) Wang et al [35] Lingyun et al [36] Gomes [37] Miguel et al [39] Lingyun et al [38] Kaveh and Javadi [40] Kaveh and Zolghadr [ Bold value highlight the best results for each optimization case.…”

Section: Design Variablesmentioning

confidence: 99%

“…These bounds are imposed by not sampling infeasible designs in the computer code. Further details about the construction of the truss optimization problem as well as the implementation of the [40] penalization may be found in the works by Miguel et al [24] and Kaveh and Zolghadr [42].…”

Section: Truss Optimization Problemmentioning

confidence: 99%

“…This is a benchmark problem that was extensively studied by many authors using different optimization methods [33][34][35][36][37][38][39][40][41][42]. Several of the best designs found in the literature are detailed in Table 1.…”

Section: Design Variablesmentioning

confidence: 99%

“…The effectiveness of the SGA is demonstrated through a selection of benchmark problems taken from the literature. Special attention is given to problems that involve topology optimization and natural frequency constraints [33][34][35][36][37][38][39][40][41][42] due to the complexities that they present. The first two problems consist in the size and size/shape optimization of truss structures with natural frequency constraints.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Search group algorithm: A new metaheuristic method for the optimization of truss structures

Gonçalves

López

Miguel

2015

Computers & Structures

171

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Section: Design Variablesmentioning

confidence: 98%

Section: Design Variablesmentioning

confidence: 99%

Section: Truss Optimization Problemmentioning

confidence: 99%

Section: Design Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Search group algorithm: A new metaheuristic method for the optimization of truss structures

Gonçalves

López

Miguel

2015

Computers & Structures

171

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Investigating bound handling schemes and parameter settings for the interior search algorithm to solve truss problems

Kashani

Chiong

Dhakal

et al. 2021

Engineering Reports

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The interior search algorithm (ISA) is an optimization algorithm inspired by esthetic techniques used for interior design and decoration. The algorithm has only one parameter, controlled by θ, and uses an evolutionary boundary constraint handling (BCH) strategy to keep itself within an admissible solution space while approaching the optimum. We apply the ISA to find optimal weight design of truss structures with frequency constraints. Sensitivity of the ISA's performance to the θ parameter and the BCH strategy is investigated by considering different values of θ and BCH techniques. This is the first study in the literature on the sensitivity of truss optimization problems to various BCH approaches. Moreover, we also study the impact of different BCH methods on diversification and intensification. Through extensive numerical simulations, we identified the best BCH methods that provide consistently better results for all truss problems studied, and obtained a range of θ that maximizes the ISA's performance for all problem classes studied. However, results also recommend further fine-tuning of parameter settings for specific case studies to obtain the best results.

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Constrained mean‐variance mapping optimization for truss optimization problems

Aslani

Ghasemi

Gandomi

2017

Structural Design Tall Build

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Summary Truss optimization is a complex structural problem that involves geometric and mechanical constraints. In the present study, constrained mean‐variance mapping optimization (MVMO) algorithms have been introduced for solving truss optimization problems. Single‐solution and population‐based variants of MVMO are coupled with an adaptive exterior penalty scheme to handle geometric and mechanical constraints. These tools are explained and tuned for weight minimization of trusses with 10 to 200 members and up to 1,200 nonlinear constraints. The results are compared with those obtained from the literature and classical genetic algorithm. The results show that a MVMO algorithm has a rapid rate of convergence and its final solution can obviously outperform those of other algorithms described in the literature. The observed results suggest that a constrained MVMO is an attractive tool for engineering‐based optimization, particularly for computationally expensive problems in which the rate of convergence and global convergence are important.

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Shape and size optimization of trusses with multiple frequency constraints using harmony search and ray optimizer for enhancing the particle swarm optimization algorithm

Cited by 69 publications

References 18 publications

Search group algorithm: A new metaheuristic method for the optimization of truss structures

Search group algorithm: A new metaheuristic method for the optimization of truss structures

Investigating bound handling schemes and parameter settings for the interior search algorithm to solve truss problems

Constrained mean‐variance mapping optimization for truss optimization problems

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