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

Materials Testing, Journal Year: 2024, Volume and Issue: 66(9), P. 1439 - 1448

Published: May 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

Language: Английский

---

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

Materials Testing, Journal Year: 2024, Volume and Issue: 66(8), P. 1230 - 1240

Published: July 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.

Language: Английский

---

Dream Optimization Algorithm (DOA): A novel metaheuristic optimization algorithm inspired by human dreams and its applications to real-world engineering problems

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 436, P. 117718 - 117718

Published: Jan. 9, 2025

Language: Английский

---

Parameters identification of magnetorheological damper based on particle swarm optimization algorithm

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 143, P. 110016 - 110016

Published: Jan. 13, 2025

Language: Английский

---

Artificial neural network infused quasi oppositional learning partial reinforcement algorithm for structural design optimization of vehicle suspension components

Materials Testing, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 30, 2024

Abstract This paper introduces and investigates an enhanced Partial Reinforcement Optimization Algorithm (E-PROA), a novel evolutionary algorithm inspired by partial reinforcement theory to efficiently solve complex engineering optimization problems. The proposed combines the (PROA) with quasi-oppositional learning approach improve performance of pure PROA. E-PROA was applied five distinct design components: speed reducer design, step-cone pulley weight optimization, economic cantilever beams, coupling bolted rim vehicle suspension arm An artificial neural network as metamodeling is used obtain equations for shape optimization. Comparative analyses other benchmark algorithms, such ship rescue algorithm, mountain gazelle optimizer, cheetah demonstrated superior in terms convergence rate, solution quality, computational efficiency. results indicate that holds excellent promise technique addressing

Language: Английский

---

Experimental and numerical investigation of crash performances of additively manufactured novel multi-cell crash box made with CF15PET, PLA, and ABS

Materials Testing, Journal Year: 2024, Volume and Issue: 66(9), P. 1510 - 1518

Published: Aug. 13, 2024

Abstract In this study, a novel multi-cell crash box was designed and produced using 15 % short carbon fiber reinforced polyethylene terephthalate (CF15PET), polylactic acid (PLA), acrylonitrile butadiene styrene (ABS) filaments one of the additive manufacturing methods, melt deposition method (FDM). All structures’ maximum force energy absorption performances have been investigated. As result test, it determined that box, which best meets high folding properties, expected features in boxes, has parts manufactured ABS CF15PET materials. According to test result, found is 11 higher than approximately 4.5 PLA. It response value 5 12 materials can be used boxes form an idea about design by designing analyzing finite element programs.

Language: Английский

---

Enhancing the performance of a additive manufactured battery holder using a coupled artificial neural network with a hybrid flood algorithm and water wave algorithm

Materials Testing, Journal Year: 2024, Volume and Issue: 66(10), P. 1557 - 1563

Published: Aug. 8, 2024

Abstract This research is the first attempt in literature to combine design for additive manufacturing and hybrid flood algorithms optimal of battery holders an electric vehicle. article uses a recent metaheuristic explore optimization holder A polylactic acid (PLA) material preferred during manufacturing. Specifically, both algorithm (FLA-SA) water wave optimizer (WWO) are utilized generate holder. The hybridized with simulated annealing algorithm. An artificial neural network employed acquire meta-model, enhancing efficiency. results underscore robustness achieving designs car components, suggesting its potential applicability various product development processes.

Language: Английский

---

Tactical flight optimizer: a novel optimization technique tested on mathematical, mechanical, and structural optimization problems

Materials Testing, Journal Year: 2025, Volume and Issue: 67(2), P. 330 - 352

Published: Jan. 22, 2025

Abstract The current study presents a novel gradient-free metaheuristic search algorithm named Tactical Flight Optimizer (TFO), tailored to meet the growing need for high-performance optimization techniques in solving complex engineering and mathematical problems. main contribution of this is development method that simulates tactical air combat formations, offering sophisticated alternative conventional algorithms. In proposed method, location each agent updated based on resultant vector derived from three updating vectors. vectors incorporate total information stored by agents iteration. Consequently, navigation process guided more logical mechanism rather than simple random process. performance TFO initially benchmarked set constrained functions. Subsequently, it evaluated addressing suite mechanical structural problems, containing both discrete continuous decision variables. obtained results are compared with five other well-stablished techniques. Acquired numerical indicate can provide promising problems terms computational cost, accuracy, stability.

Language: Английский

---

Fishing cat optimizer: a novel metaheuristic technique

Engineering Computations, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Purpose The fishing cat's unique hunting strategies, including ambush, detection, diving and trapping, inspired the development of a novel metaheuristic optimization algorithm named Fishing Cat Optimizer (FCO). purpose this paper is to introduce FCO, offering fresh perspective on demonstrating its potential for solving complex problems. Design/methodology/approach FCO structures process into four distinct phases. Each phase incorporates tailored search strategy enrich diversity population attain an optimal balance between extensive global exploration focused local exploitation. Findings To assess efficacy algorithm, we conducted comparative analysis with state-of-the-art algorithms, COA, WOA, HHO, SMA, DO ARO, using test suite comprising 75 benchmark functions. findings indicate that achieved results 88% functions, whereas SMA which ranked second, excelled only 21% Furthermore, secured average ranking 1.2 across sets CEC2005, CEC2017, CEC2019 CEC2022, superior convergence capability robustness compared other comparable algorithms. Research limitations/implications Although performs excellently in single-objective problems constrained problems, it also has some shortcomings defects. First, structure relatively there are many parameters. value parameters certain impact Second, computational complexity high. When high-dimensional takes more time than algorithms such as GWO WOA. Third, although multimodal rarely obtains theoretical solution when combinatorial Practical implications applied five common engineering design Originality/value This innovatively proposes mimics mechanisms cats, strategies lurking, perceiving, rapid precise trapping. These abstracted closely connected iterative stages, corresponding in-depth exploration, multi-dimensional fine developmental localized refinement contraction search. enables efficient fine-tuning environments, significantly enhancing algorithm's adaptability efficiency.

Language: Английский

---

Frequency Regulation of Two-Area Thermal and Photovoltaic Power System via Flood Algorithm

Results in Control and Optimization, Journal Year: 2025, Volume and Issue: 18, P. 100539 - 100539

Published: Feb. 21, 2025

Language: Английский

---