Heuristic dragonfly algorithm for optimal design of truss structures with discrete variables

Jawad, Farqad K. J.; Mahmood, Mohammed; Wang, Dansheng; Al-Azzawi, Osama; Al-Jamely, Anas

doi:10.1016/j.istruc.2020.11.071

Cited by 25 publications

(6 citation statements)

References 37 publications

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“…Inherent to the algorithm was the Lévy motion model, and conclusions were made favoring the motion pattern for truss optimization. A similar conclusion was reached with implementing the Dragonfly Algorithm (DA) by Jawad et al for truss weight minimization [29]. Lévy flight techniques (intrinsic to DA operations) proved crucial to the algorithm's excellent performance, allowing it to reach global truss solutions with the least computational effort.…”

Section: Motivation and Contributionmentioning

confidence: 60%

Marine Predators Algorithm for Sizing Optimization of Truss Structures with Continuous Variables

Bodalal

Shuaeib

2023

Computation

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In this study, the newly developed Marine Predators Algorithm (MPA) is formulated to minimize the weight of truss structures. MPA is a swarm-based metaheuristic algorithm inspired by the efficient foraging strategies of marine predators in oceanic environments. In order to assess the robustness of the proposed method, three normal-sized structural benchmarks (10-bar, 60-bar, and 120-bar spatial dome) and three large-scale structures (272-bar, 942-bar, and 4666-bar truss tower) were selected from the literature. Results point to the inherent strength of MPA against all state-of-the-art metaheuristic optimizers implemented so far. Moreover, for the first time in the field, a quantitative evaluation and an answer to the age-old question of the proper convergence behavior (exploration vs. exploitation balance) in the context of structural optimization is conducted. Therefore, a novel dimension-wise diversity index is adopted as a methodology to investigate each of the two schemes. It was concluded that the balance that produced the best results was about 90% exploitation and 10% exploration (on average for the entire computational process).

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Section: Motivation and Contributionmentioning

confidence: 60%

Marine Predators Algorithm for Sizing Optimization of Truss Structures with Continuous Variables

Bodalal

Shuaeib

2023

Computation

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“…Aghelpour [103] used the DA to optimize the parameters of a support vector machine (SVM) and produce a hybrid SVM-DA model, which improved the accuracy of the SVM in Palmer drought severity index prediction by 29%. Jawad [104] made some amendments to the DA, to solve the discrete function of the optimization problem, improve the algorithm performance, and design a more suitable truss structure. Urooj [105][106] implemented of the dragonfly optimization algorithm to track the maximum power from solar radiation; this involved concepts from machine learning, image processing, and data computation.…”

Section: Research Progress Regarding Theory and Application Of Dragon...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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“…It mimics the static and dynamic swarming behaviours of dragonflies. Since its inception in 2015, DA has been applied to solve various classes of optimization problems ranging from continuous (Abedi and Gharehchopogh, 2020) to discrete (Jawad et al, 2021) and from unconstrained (Can and Alatas, 2017) to constrained (Khalilpourazari and Khalilpourazary, 2020). It has been successfully employed in both single-objective (Reddy, 2016) and multi-objective roles (Joshi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy MARCOS-Based Analysis of Dragonfly Algorithm Variants in Industrial Optimization Problems

Kalita,

Ganesh,

Shankar

et al. 2023

Informatica

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Metaheuristics are commonly employed as a means of solving many distinct kinds of optimization problems. Several natural-process-inspired metaheuristic optimizers have been introduced in the recent years. The convergence, computational burden and statistical relevance of metaheuristics should be studied and compared for their potential use in future algorithm design and implementation. In this paper, eight different variants of dragonfly algorithm, i.e. classical dragonfly algorithm (DA), hybrid memory-based dragonfly algorithm with differential evolution (DADE), quantum-behaved and Gaussian mutational dragonfly algorithm (QGDA), memory-based hybrid dragonfly algorithm (MHDA), chaotic dragonfly algorithm (CDA), biogeography-based Mexican hat wavelet dragonfly algorithm (BMDA), hybrid Nelder-Mead algorithm and dragonfly algorithm (INMDA), and hybridization of dragonfly algorithm and artificial bee colony (HDA) are applied to solve four industrial chemical process optimization problems. A fuzzy multi-criteria decision making tool in the form of fuzzy-measurement alternatives and ranking according to compromise solution (MARCOS) is adopted to ascertain the relative rankings of the DA variants with respect to computational time, Friedman’s rank based on optimal solutions and convergence rate. Based on the comprehensive testing of the algorithms, it is revealed that DADE, QGDA and classical DA are the top three DA variants in solving the industrial chemical process optimization problems under consideration.

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Heuristic dragonfly algorithm for optimal design of truss structures with discrete variables

Cited by 25 publications

References 37 publications

Marine Predators Algorithm for Sizing Optimization of Truss Structures with Continuous Variables

Marine Predators Algorithm for Sizing Optimization of Truss Structures with Continuous Variables

A survey on several new popular swarm intelligence optimization algorithms

A Fuzzy MARCOS-Based Analysis of Dragonfly Algorithm Variants in Industrial Optimization Problems

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