A review of classical methods and Nature-Inspired Algorithms (NIAs) for optimization problems

Mandal, Pawan Kumar

doi:10.1016/j.rico.2023.100315

Cited by 12 publications

(2 citation statements)

References 190 publications

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“…The application of optimization algorithms in the field of bridge engineering can solve design problems with room for improvement, thus significantly increasing the accuracy of the analysis, helping engineers to better understand and predict the behavior of bridges, and improving the efficiency and safety of bridge design and maintenance [9][10][11]. Commonly used optimization algorithms include the genetic algorithm, the particle swarm optimization algorithm, the simulated annealing algorithm, and so on [12,13]. The genetic algorithm is a computational model used to search for the optimal solution by simulating the natural evolution process and has good robustness and global search ability when solving multi-objective optimization problems [14,15].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Traffic Load Model for Suspenders of a Super-Long-Span Suspension Bridge Considering Influence Line Geometry and Extreme Load Effect Scenarios

Wei,

Ruan,

et al. 2024

Applied Sciences

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The reasonable expression of live load and its accuracy are important to the safety and design rationality of highway bridge structures. In this study, the optimization issue of the traffic load model for the suspenders of large-scale suspension bridges is studied. Taking a 2300-m main span suspension bridge as an example, a method for suspender classification based on the geometric feature of the influence lines is proposed, and the extreme traffic load effect scenarios are analyzed and used as an optimization reference. Multi-objective optimization based on a genetic algorithm is used to explore the improvement of the traffic load model of the suspender. The traffic load model of the suspender is optimized with three objectives, i.e., accuracy, convenience, and improvement, and the optimization results regarding the load value and loading length are obtained. The value of the uniformly distributed load of the optimized model ranges from 6.4 kN/m to 8.9 kN/m, and the maximum value of the concentrated force could reach 1433 kN. By comparing the obtained optimized model with the current specification model and the extreme load effect scenario model, the improved applicability of the optimized model in the analysis of the load effect of the suspender can be verified. The optimized method and relevant conclusions can provide useful references for the improved design and operation management of similar bridge structures.

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Section: Introductionmentioning

confidence: 99%

Optimization of the Traffic Load Model for Suspenders of a Super-Long-Span Suspension Bridge Considering Influence Line Geometry and Extreme Load Effect Scenarios

Wei,

Ruan,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In quadratic programming, we want to determine the variable values that minimize or maximize the quadratic objective function that satisfies several linear constraints [2]. There are two ways to solve quadratic programming problems: the classical method [3] and the heuristic method [4]. This research uses two methods, namely the classical method using Wolfe's method, while the heuristic method uses the particle swarm optimization (PSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Application of Quadratic Programming on Portfolio Optimization Using Wolfe’s Method and Particle Swarm Optimization Algorithm

Syaripuddin,

Amijaya,

Wasono

et al. 2024

BAREKENG: J. Math. & App.

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Stock portfolios can be modeled into quadratic programming problems using the Markowitz mean-variance model. Quadratic programming problems can be solved using two methods, namely classical and heuristic methods. In this research, the classical method uses Wolfe’s method, while the heuristic method uses the particle swarm optimization (PSO) algorithm. This research aims to determine optimal results in portfolio problems using two methods, namely Wolfe’s method and the PSO algorithm. The data used in this research is data from 10 stock companies that distribute the highest dividends in the IDX High Dividend 20 category for the 2022 period. The research results discuss the portfolios of PT Astra International Tbk and PT. Indo Tambangraya Megah Tbk. Based on the result, using Wolfe’s method, the ASII and ITMG stock portfolios are obtained, namely the optimal proportion of ASII shares = 0.76401 or 76.401% and ITMG shares = 0.23598 or 23.598%, while the PSO algorithm obtains a portfolio of ASII and ITMG shares, namely ASII shares = 75.02% and ITMG shares = 24.98%. Compared to Wolfe’s method, the PSO algorithm has a smaller Z value 5.7.

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A Novel Bi-objective Credibilistic Mean–Semivariance Portfolio Selection Problem with Coherent Triangular Fuzzy Numbers

Mandal

2024

Lecture Notes in Networks and Systems

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A review of classical methods and Nature-Inspired Algorithms (NIAs) for optimization problems

Cited by 12 publications

References 190 publications

Optimization of the Traffic Load Model for Suspenders of a Super-Long-Span Suspension Bridge Considering Influence Line Geometry and Extreme Load Effect Scenarios

Optimization of the Traffic Load Model for Suspenders of a Super-Long-Span Suspension Bridge Considering Influence Line Geometry and Extreme Load Effect Scenarios

Application of Quadratic Programming on Portfolio Optimization Using Wolfe’s Method and Particle Swarm Optimization Algorithm

A Novel Bi-objective Credibilistic Mean–Semivariance Portfolio Selection Problem with Coherent Triangular Fuzzy Numbers

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