Optimum design of additively manufactured aerospace components with different lattice structures

Materials Testing, Год журнала: 2024, Номер 66(6), С. 876 - 882

Опубликована: Март 13, 2024

Abstract Nowadays, the need for new technologies is increasing, especially to find solutions inadequacies in production of complex structures. The additive manufacturing methods developed facilitate parts and move technology forward with factors such as cost efficiency. With optimization designed by methods, it possible obtain optimum product even most At end process, final desired properties obtained a result part size tolerance precision optimizations. In this study, lattice applied passenger aircraft bracket. It aimed reduce weight and, at same time, increase efficiency optimizing For purpose, Altair Inspire program was used, variation mass, displacement, safety coefficient, stress values according different structures were investigated.

Язык: Английский

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An improved algorithm optimization algorithm based on RungeKutta and golden sine strategy

Expert Systems with Applications, Год журнала: 2024, Номер 247, С. 123262 - 123262

Опубликована: Янв. 25, 2024

Язык: Английский

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An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement

Expert Systems with Applications, Год журнала: 2022, Номер 215, С. 119421 - 119421

Опубликована: Дек. 9, 2022

Язык: Английский

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A global optimizer inspired from the survival strategies of flying foxes

Engineering With Computers, Год журнала: 2022, Номер 39(2), С. 1583 - 1616

Опубликована: Янв. 3, 2022

Язык: Английский

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An Improved Wild Horse Optimizer for Solving Optimization Problems

Mathematics, Год журнала: 2022, Номер 10(8), С. 1311 - 1311

Опубликована: Апрель 14, 2022

Wild horse optimizer (WHO) is a recently proposed metaheuristic algorithm that simulates the social behavior of wild horses in nature. Although WHO shows competitive performance compared to some algorithms, it suffers from low exploitation capability and stagnation local optima. This paper presents an improved (IWHO), which incorporates three improvements enhance optimizing capability. The main innovation this put forward random running strategy (RRS) competition for waterhole mechanism (CWHM). employed balance exploration exploitation, boost behavior. Moreover, dynamic inertia weight (DIWS) utilized optimize global solution. IWHO evaluated using twenty-three classical benchmark functions, ten CEC 2021 test five real-world optimization problems. High-dimensional cases (D = 200, 500, 1000) are also tested. Comparing nine well-known experimental results functions demonstrate very terms convergence speed, precision, accuracy, stability. Further, practical method verified by engineering design

Язык: Английский

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An Improved Teaching-Learning-Based Optimization Algorithm with Reinforcement Learning Strategy for Solving Optimization Problems

Computational Intelligence and Neuroscience, Год журнала: 2022, Номер 2022, С. 1 - 24

Опубликована: Март 24, 2022

This paper presents an improved teaching-learning-based optimization (TLBO) algorithm for solving problems, called RLTLBO. First, a new learning mode considering the effect of teacher is presented. Second, Q-Learning method in reinforcement (RL) introduced to build switching mechanism between two different modes learner phase. Finally, ROBL adopted after both and phases improve local optima avoidance ability These strategies effectively enhance convergence speed accuracy proposed algorithm. RLTLBO analyzed on 23 standard benchmark functions eight CEC2017 test verify performance. The results reveal that provides effective efficient performance functions. Moreover, also applied solve industrial engineering design problems. Compared with basic TLBO seven state-of-the-art algorithms, illustrate has superior promising prospects dealing real-world source codes are publicly available at https://github.com/WangShuang92/RLTLBO.

Язык: Английский

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Niching chimp optimization for constraint multimodal engineering optimization problems

Expert Systems with Applications, Год журнала: 2022, Номер 198, С. 116887 - 116887

Опубликована: Март 16, 2022

Язык: Английский

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EOSMA: An Equilibrium Optimizer Slime Mould Algorithm for Engineering Design Problems

Arabian Journal for Science and Engineering, Год журнала: 2022, Номер 47(8), С. 10115 - 10146

Опубликована: Янв. 6, 2022

Язык: Английский

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DTSMA: Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm

Mathematical Biosciences & Engineering, Год журнала: 2022, Номер 19(3), С. 2240 - 2285

Опубликована: Янв. 1, 2022

<abstract> <p>The slime mould algorithm (SMA) is a metaheuristic recently proposed, which inspired by the oscillations of mould. Similar to other algorithms, SMA also has some disadvantages such as insufficient balance between exploration and exploitation, easy fall into local optimum. This paper, an improved based on dominant swarm with adaptive t-distribution mutation (DTSMA) proposed. In DTSMA, used SMA's convergence speed, balances enhanced exploitation ability. addition, new mechanism hybridized increase diversity populations. The performances DTSMA are verified CEC2019 functions eight engineering design problems. results show that for functions, best; problems, obtains better than many algorithms in literature when constraints satisfied. Furthermore, solve inverse kinematics problem 7-DOF robot manipulator. overall strong optimization Therefore, promising global problems.</p> </abstract>

Язык: Английский

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An opposition learning and spiral modelling based arithmetic optimization algorithm for global continuous optimization problems

Engineering Applications of Artificial Intelligence, Год журнала: 2022, Номер 113, С. 104981 - 104981

Опубликована: Май 31, 2022

Язык: Английский

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