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

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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, Год журнала: 2025, Номер 436, С. 117718 - 117718

Опубликована: Янв. 9, 2025

Язык: Английский

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

Materials Testing, Год журнала: 2024, Номер unknown

Опубликована: Авг. 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

Язык: Английский

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Parameters identification of magnetorheological damper based on particle swarm optimization algorithm

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 143, С. 110016 - 110016

Опубликована: Янв. 13, 2025

Язык: Английский

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

Materials Testing, Год журнала: 2024, Номер 66(9), С. 1510 - 1518

Опубликована: Авг. 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.

Язык: Английский

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A force neural network framework for structural optimization

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 143, С. 109991 - 109991

Опубликована: Янв. 11, 2025

Язык: Английский

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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, Год журнала: 2024, Номер 66(10), С. 1557 - 1563

Опубликована: Авг. 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.

Язык: Английский

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Boosting crayfish algorithm based on halton adaptive quadratic interpolation and piecewise neighborhood for complex optimization problems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 432, С. 117429 - 117429

Опубликована: Окт. 9, 2024

Язык: Английский

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Short-term wind power prediction based on IBOA-AdaBoost-RVM

Journal of King Saud University - Science, Год журнала: 2024, Номер 36(11), С. 103550 - 103550

Опубликована: Ноя. 22, 2024

Язык: Английский

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

Materials Testing, Год журнала: 2025, Номер 67(2), С. 330 - 352

Опубликована: Янв. 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.

Язык: Английский

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