Multiobjective meta-heuristic with iterative parameter distribution estimation for aeroelastic design of an aircraft wing

Wansasueb, Kittinan; Pholdee, Nantiwat; Panagant, Natee; Bureerat, Sujin

doi:10.1007/s00366-020-01077-w

Cited by 16 publications

(7 citation statements)

References 57 publications

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“…For a fair comparison, the population size of all algorithms was set to 100, the maximum number of iterations was set to 1000, the archive size was set to 100, and each example was independently run 20 times. Since the true PF is unknown, the hypervolume (HV) metric 85 , 86 was used to evaluate the performance difference of the algorithms. The HV metric can evaluate both the advancement and distribution of the obtained PF simultaneously 87 .…”

Section: Resultsmentioning

confidence: 99%

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

Yin

Luo

Zhou

et al. 2022

Sci Rep

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In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anisotropic search of the slime mould algorithm to improve the search efficiency. Then, the greedy strategy is used to update the individual and global historical optimal to accelerate the algorithm’s convergence. Finally, the random difference mutation operator is added to EOSMA to increase the probability of escaping from the local optimum. On this basis, a multi-objective EOSMA (MOEOSMA) is proposed. Then, EOSMA and MOEOSMA are applied to the IK of the 7 degrees of freedom manipulator in two scenarios and compared with 15 single-objective and 9 multi-objective algorithms. The results show that EOSMA has higher accuracy and shorter computation time than previous studies. In two scenarios, the average convergence accuracy of EOSMA is 10e−17 and 10e−18, and the average solution time is 0.05 s and 0.36 s, respectively.

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Section: Resultsmentioning

confidence: 99%

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

Yin

Luo

Zhou

et al. 2022

Sci Rep

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“…The algorithms adopted in this paper are all based on differential evolution, namely, (i) the success history-based adaptive multi-objective differential evolution (SHAMODE) introduced by [37]; (ii) the success history-based adaptive multi-objective differential evolution with whale optimization (SHAMODE-WO), which incorporates the spiral movement from the whale optimization algorithm (WOA) proposed by [42], also developed by [37]; and (iii) the multi-objective meta-heuristic with iterative parameter distribution estimation (MMIPDE) proposed by [38]. These MOEAs can be summarized as follows:…”

Section: Multi-objective Evolutionary Algorithms Adoptedmentioning

confidence: 99%

“…In this paper, MOEAs based on differential evolution (DE) [36] were adopted to solve the MOSOPs: success history-based adaptive multi-objective differential evolution (SHAMODE), SHAMODE with whale optimization (SHAMODE-WO) from reference [37], and a multi-objective meta-heuristic with iterative parameter distribution estimation (MMIPDE) [38]. To evaluate their performance, these MOEAs were recently assessed in solving MOSOPs [24], and their results were compared with the results obtained in the literature.…”

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confidence: 99%

Automatic Column Grouping of 3D Steel Frames via Multi-Objective Structural Optimization

Resende,

Martha,

Lemonge

et al. 2024

Buildings

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Formulations of structural optimization problems are proposed in this paper to automatically find the best grouping of columns in 3D steel buildings. In these formulations, the conflicting objective functions, minimized simultaneously, are the weight of the structure and the number of different groups of columns. In other words, the smaller the number of different groups of columns, the greater the weight of the structure, and the greater the number of groups, the smaller the structure’s weight. The design variables are the bracing system configuration, column cross-section orientation, and assigned W-shaped profile indices for columns, beams, and braces. The design constraints are the allowable displacements, strength, and geometric considerations. After solving the multi-objective optimization problem, the result is a Pareto front, presenting non-dominated solutions. Three evolutionary algorithms based on differential evolution are adopted in this paper to solve three computational experiments. Even if preliminary groupings of columns are adopted, considering architectural aspects such as the symmetry of the structure, it is possible to discover other interesting structural configurations that will be available to the decision maker, who will be able to make their choices based on the impacts on manufacturing, cutting, transporting, checking and welding.

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“…Optimization techniques based on metaheuristic or evolutionary algorithms, on the other hand, have also been successfully applied for both single-objective [32,33] and MO problems [34,35]. Although numerous investigations on TO of morphing wings have been studied, most of the work focuses on investigating the performance of an aeroelasticity analysis tool, an optimization solver tool, and an optimum structure.…”

Section: Introductionmentioning

confidence: 99%

Ground Structures-Based Topology Optimization of a Morphing Wing Using a Metaheuristic Algorithm

Winyangkul

Wansaseub

Sleesongsom

et al. 2021

Metals

Self Cite

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This paper presents multi-objective topology and sizing optimization of a morphing wing structure. The purpose of this paper is to design a new aircraft wing structure with a tapered shape for ribs, spars, and skins including a torsion beam for external actuating torques, which is anticipated to modify the aeroelastic characteristic of the aircraft wing using multi-objective optimization. Two multi-objective topology optimization problems are proposed employing ground element structures with high- and low-grid resolutions. The design problem is to minimize mass, maximize difference of lift effectiveness, and maximize the buckling factor of an aircraft wing subject to aeroelastic and structural constraints including lift effectiveness, critical speed, and buckling factors. The design variables include aircraft wing structure dimensions and thickness distribution. The proposed optimization problems are solved by an efficient multi-objective metaheuristic algorithm while the results are compared and discussed. The Pareto optimal fronts obtained for all tests were compared based on a hypervolume metric. The objective function values for Case I and Case II at 10 selected optimal solutions exhibit a range of structural mass as 115.3216–411.6250 kg, 125.0137–440.5869 kg, lift effectiveness as 1.0514–1.1451, 1.0834–1.1639 and bucking factor as 38.895–1133.1864 Hz, 158.1264–1844.4355 Hz, respectively. The best results reveal unconventional aircraft wing structures that can be manufactured using additive manufacturing. This research is expected to serve as a foundation for future research into multi-objective topology optimization of morphing wing structures based on the ground element framework.

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Multiobjective meta-heuristic with iterative parameter distribution estimation for aeroelastic design of an aircraft wing

Cited by 16 publications

References 57 publications

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

An equilibrium optimizer slime mould algorithm for inverse kinematics of the 7-DOF robotic manipulator

Automatic Column Grouping of 3D Steel Frames via Multi-Objective Structural Optimization

Ground Structures-Based Topology Optimization of a Morphing Wing Using a Metaheuristic Algorithm

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