Optimum design of a seat bracket using artificial neural networks and dandelion optimization algorithm

Materials Testing, Год журнала: 2023, Номер 65(12), С. 1767 - 1775

Опубликована: Окт. 13, 2023

Abstract Nature-inspired metaheuristic algorithms are gaining popularity with their easy applicability and ability to avoid local optimum points, they spreading wide application areas. Meta-heuristic optimization used achieve an design in engineering problems aiming obtain lightweight designs. In this article, structural methods the process of achieving a seat bracket. As result topology optimization, new concept bracket was created shape optimization. mass stress values obtained depending on variables, constraint, objective functions were by using artificial neural networks. The problem based minimization is solved applying dandelion algorithm verified finite element analysis.

Язык: Английский

---

Red-tailed hawk algorithm for numerical optimization and real-world problems

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Авг. 9, 2023

This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk (RTH). As predator, has hunting strategy from detecting prey until swoop stage. There are three stages during process. In high soaring stage, explores search space and determines area with location. low moves inside selected around to choose best position for hunt. Then, swings hits its target in stooping swooping stages. The proposed mimics prey-hunting method of solving real-world problems. performance RTH been evaluated on classes first class includes specific kinds problems: 22 standard benchmark functions, including unimodal, multimodal, fixed-dimensional multimodal IEEE Congress Evolutionary Computation 2020 (CEC2020), CEC2022. is compared eight recent algorithms confirm contribution these considered Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle (MGO), Gorilla Troops (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila (AO), Harris Hawks (HHO). results regarding accuracy, robustness, convergence speed. second seven engineering problems that will be investigate other published profoundly. Finally, proton exchange membrane fuel cell (PEMFC) extraction parameters performed evaluate complex problem. several papers approve performance. ultimate each ability provide higher most cases. For class, mostly got optimal solutions functions faster provided better third when resolving real word or extracting PEMFC parameters.

Язык: Английский

---

A new enhanced mountain gazelle optimizer and artificial neural network for global optimization of mechanical design problems

Materials Testing, Год журнала: 2024, Номер 66(4), С. 544 - 552

Опубликована: Янв. 24, 2024

Abstract Nature-inspired metaheuristic optimization algorithms have many applications and are more often studied than conventional techniques. This article uses the mountain gazelle optimizer, a recently created algorithm, artificial neural network to optimize mechanical components in relation vehicle component optimization. The family formation, territory-building, food-finding strategies of gazelles serve as major inspirations for algorithm. In order various engineering challenges, base algorithm (MGO) is hybridized with Nelder–Mead (HMGO-NM) current work. considered was applied solve four different categories, namely automobile, manufacturing, construction, tasks. Moreover, obtained results compared terms statistics well-known algorithms. findings show dominance over rest optimizers. being said HMGO can be common range industrial real-world problems.

Язык: Английский

---

Optimal design of structural engineering components using artificial neural network-assisted crayfish algorithm

Materials Testing, Год журнала: 2024, Номер 66(9), С. 1439 - 1448

Опубликована: Май 24, 2024

Abstract Optimization techniques play a pivotal role in enhancing the performance of engineering components across various real-world applications. Traditional optimization methods are often augmented with exploitation-boosting due to their inherent limitations. Recently, nature-inspired algorithms, known as metaheuristics (MHs), have emerged efficient tools for solving complex problems. However, these algorithms face challenges such imbalance between exploration and exploitation phases, slow convergence, local optima. Modifications incorporating oppositional techniques, hybridization, chaotic maps, levy flights been introduced address issues. This article explores application recently developed crayfish algorithm (COA), assisted by artificial neural networks (ANN), design optimization. The COA, inspired foraging migration behaviors, incorporates temperature-dependent strategies balance phases. Additionally, ANN augmentation enhances algorithm’s accuracy. COA method optimizes components, including cantilever beams, hydrostatic thrust bearings, three-bar trusses, diaphragm springs, vehicle suspension systems. Results demonstrate effectiveness achieving superior solutions compared other emphasizing its potential diverse

Язык: Английский

---

Modified crayfish optimization algorithm for solving multiple engineering application problems

Artificial Intelligence Review, Год журнала: 2024, Номер 57(5)

Опубликована: Апрель 24, 2024

Abstract Crayfish Optimization Algorithm (COA) is innovative and easy to implement, but the crayfish search efficiency decreases in later stage of algorithm, algorithm fall into local optimum. To solve these problems, this paper proposes an modified optimization (MCOA). Based on survival habits crayfish, MCOA environmental renewal mechanism that uses water quality factors guide seek a better environment. In addition, integrating learning strategy based ghost antagonism enhances its ability evade optimality. evaluate performance MCOA, tests were performed using IEEE CEC2020 benchmark function experiments conducted four constraint engineering problems feature selection problems. For constrained improved by 11.16%, 1.46%, 0.08% 0.24%, respectively, compared with COA. average fitness value accuracy are 55.23% 10.85%, respectively. shows solving complex spatial practical application The combination environment updating significantly improves MCOA. This discovery has important implications for development field optimization. Graphical

Язык: Английский

---

Optimization of truss structures using multi-objective cheetah optimizer

Mechanics Based Design of Structures and Machines, Год журнала: 2024, Номер unknown, С. 1 - 22

Опубликована: Авг. 8, 2024

In this study, a multi-objective version of the recently proposed cheetah optimizer called (MOCO) has been proposed. MOCO draws inspiration from targeted hunting strategy employed by cheetahs, which involves sequence actions: searching for prey, patiently waiting right moment to attack, swiftly launching and then retreating prey returning their habitat. is result modification enhancement its single-objective counterpart, utilizing Pareto dominance-based approach. This adaptation allows efficiently handle multiple objectives, explores exploits promising areas in optimization landscape, identifies non-dominated solutions, offering valuable tradeoff choices decision-makers. To demonstrate practical applications, method address five intricate structural design problems. These problems involve pair competing objectives: minimization weight reduction maximum nodal displacement. gauge efficacy efficiency algorithm, comparative analysis conducted against three alternative state-of-the-art algorithms. Furthermore, rigorous evaluation carried out hypervolume testing. The findings reveal that algorithm surpasses performance other algorithms, underscored capacity uncover diverse array solutions. delve deeper into experimental results, statistical employing Friedman's rank test employed. solutions generated convergence patterns exhibited approach underscore exceptional proficiency resolving

Язык: Английский

---

Crash performance of a novel bio-inspired energy absorber produced by additive manufacturing using PLA and ABS materials

Materials Testing, Год журнала: 2024, Номер 66(5), С. 696 - 704

Опубликована: Фев. 19, 2024

Abstract Thin-walled structures are one of the important safety components used in vehicles. They placed front parts vehicles to minimize impacts that occur event a collision, and they absorb impact force by changing shape collision. Crash boxes have high-impact absorption, low weight, low-cost expectations. In design crash boxes, thin-walled preferred due their high deformation capability. this study, additive manufacturing method was produce structures. were produced methods using PLA ABS materials. The manufactured tested an test. experimental results, energy absorption ability from materials examined, fragility observed. results verified finite element analysis made

Язык: Английский

---

Enhancing the structural performance of engineering components using the geometric mean optimizer

Materials Testing, Год журнала: 2024, Номер 66(7), С. 1063 - 1073

Опубликована: Апрель 30, 2024

Abstract In this article, a newly developed optimization approach based on mathematics technique named the geometric mean algorithm is employed to address challenge of robot gripper, airplane bracket, and suspension arm automobiles, followed by an additional three engineering problems. Accordingly, other challenges are ten-bar truss, three-bar tubular column, spring systems. As result, demonstrates promising statistical outcomes when compared well-established algorithms. Additionally, it requires less iteration achieve global optimum solution. Furthermore, exhibits minimal deviations in results, even techniques produce better or similar outcomes. This suggests that proposed paper can be effectively utilized for wide range critical industrial real-world challenges.

Язык: Английский

---

Optimization of electric vehicle design problems using improved electric eel foraging optimization algorithm

Materials Testing, Год журнала: 2024, Номер 66(8), С. 1230 - 1240

Опубликована: Июль 5, 2024

Abstract This paper introduces a novel approach, the Modified Electric Eel Foraging Optimization (EELFO) algorithm, which integrates artificial neural networks (ANNs) with metaheuristic algorithms for solving multidisciplinary design problems efficiently. Inspired by foraging behavior of electric eels, algorithm incorporates four key phases: interactions, resting, hunting, and migrating. Mathematical formulations each phase are provided, enabling to explore exploit solution spaces effectively. The algorithm’s performance is evaluated on various real-world optimization problems, including weight engineering components, economic pressure handling vessels, cost welded beams. Comparative analyses demonstrate superiority MEELFO in achieving optimal solutions minimal deviations computational effort compared existing methods.

Язык: Английский

---

Experimental analysis of the effects of different production directions on the mechanical characteristics of ABS, PLA, and PETG materials produced by FDM

Materials Testing, Год журнала: 2024, Номер 66(2), С. 198 - 206

Опубликована: Янв. 8, 2024

Abstract One of the most researched technologies among used for producing complex and diverse parts today is additive manufacturing. In manufacturing, production can be carried out using thermoplastic metal materials without requiring an additional process. Among manufacturing technologies, Fused Filament Fabrication (FFF) method widely worldwide due to its affordability broad application area. FFF a in which part formation achieved by depositing melted on each other. recent years, polymer such as polylactic acid (PLA), polyethylene terephthalate glycol (PETG), acrylonitrile butadiene styrene (ABS) have been frequently many industrial areas because they are lightweight, inexpensive, sustainable, provide sufficient strength engineering applications. This study conducted tensile, three-point bending, Charpy, compression tests PLA, PETG, ABS at angles 15°–75° 30°–60°, results were compared.

Язык: Английский

---