A new neural network–assisted hybrid chaotic hiking optimization algorithm for optimal design of engineering components

Materials Testing, Год журнала: 2025, Номер unknown

Опубликована: Апрель 11, 2025

Abstract In the era of artificial intelligence (AI), optimization and parametric studies engineering structural systems have become feasible tasks. AI ML (machine learning) offer advantages over classical techniques, which often face challenges such as slower convergence, difficulty handling multiobjective functions, high computational time. Modern techniques may not effectively address all critical design problems despite these advancements. Nature-inspired algorithms based on physical phenomena in nature, human behavior, swarm intelligence, evolutionary principles present a viable alternative for multidisciplinary challenges. This article explores various using newly developed modified hiking algorithm (HOA). The is inspired by hill climbing hiker speed. HOA are compared with those several famous from literature, demonstrating superior results terms statistical measures.

Язык: 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, 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

---

Deep Learning Perspective on the Construction of SPOC Teaching Model of Music and Dance in Colleges and Universities

Systems and Soft Computing, Год журнала: 2024, Номер 6, С. 200137 - 200137

Опубликована: Авг. 23, 2024

Язык: Английский

---

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

---

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

---

Real-Time Data-Driven Estimation of Production for Point Bottom Sealing and Cutting Machines Using Machine Learning

Systems and Soft Computing, Год журнала: 2025, Номер unknown, С. 200194 - 200194

Опубликована: Фев. 1, 2025

Язык: Английский

---

Fishing cat optimizer: a novel metaheuristic technique

Engineering Computations, Год журнала: 2025, Номер unknown

Опубликована: Фев. 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.

Язык: Английский

---

Research and Application of Optimization of Physical Education Training Model Based on Multi-Objective Differential Evolutionary Algorithm

Systems and Soft Computing, Год журнала: 2025, Номер unknown, С. 200200 - 200200

Опубликована: Фев. 1, 2025

Язык: Английский

---

Efficient Optimization of Engineering Problems With A Particular Focus on High‐Order IIR Modeling for System Identification Using Modified Dandelion Optimizer

Optimal Control Applications and Methods, Год журнала: 2025, Номер unknown

Опубликована: Фев. 24, 2025

ABSTRACT This paper introduces the modified dandelion optimizer (mDO), a novel adaptive metaheuristic algorithm designed to address complex engineering optimization challenges, with focus on infinite impulse response (IIR) system identification. The proposed mDO incorporates three key advancements: an enhanced descending phase improve global exploration, exploration‐exploitation that balances search intensity and breadth, self‐adaptive crossover operator refines solutions dynamically. These innovations specifically target challenges associated high‐order IIR modeling, enabling deliver more precise efficient To validate its performance, was rigorously evaluated across diverse testing environments, including CEC2017 CEC2022 benchmark functions, various model identification scenarios, real‐world design problems such as multi‐product batch plant design, multiple disk clutch brake speed reducer design. Comparative analyses reveal consistently outperforms leading algorithms in terms of accuracy, robustness, computational efficiency, particularly complex, high‐dimensional landscapes. Statistical assessments further confirm mDO's superior capability accurately identifying parameters even under noise varying orders. study positions competitive versatile tool for applications, offering significant improvements accuracy adaptability advanced modeling problem‐solving.

Язык: Английский

---

The improved mountain gazelle optimizer for spatiotemporal support vector regression: a novel method for railway subgrade settlement prediction integrating multi-source information

Applied Intelligence, Год журнала: 2025, Номер 55(6)

Опубликована: Март 4, 2025

Язык: Английский

---