Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism

Zhou, Xiangbing; Ma, Huili; Gu, Jianggang; Chen, Huiling; Deng, Wu

doi:10.1016/j.engappai.2022.105139

Cited by 146 publications

(55 citation statements)

References 68 publications

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“…In the future, applying the proposed algorithm to optimize the real-world complex engineering problems [52][53][54] has become the key research direction for the next step. At the same time, the methods and related applications based on parameter adaptation are also key issues to be considered [55][56][57][58].…”

Section: Discussionmentioning

confidence: 99%

Dynamic elite strategy mayfly algorithm

Zhu

2022

PLoS ONE

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The mayfly algorithm (MA), as a newly proposed intelligent optimization algorithm, is found that easy to fall into the local optimum and slow convergence speed. To address this, an improved mayfly algorithm based on dynamic elite strategy (DESMA) is proposed in this paper. Specifically, it first determines the specific space near the best mayfly in the current population, and dynamically sets the search radius. Then generating a certain number of elite mayflies within this range. Finally, the best one among the newly generated elite mayflies is selected to replace the best mayfly in the current population when the fitness value of elite mayfly is better than that of the best mayfly. Experimental results on 28 standard benchmark test functions from CEC2013 show that our proposed algorithm outperforms its peers in terms of accuracy speed and stability.

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Section: Discussionmentioning

confidence: 99%

Dynamic elite strategy mayfly algorithm

Zhu

2022

PLoS ONE

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“…Otherwise, it would be preferable to use multiple calibrations [20,32] or multibody kinematic optimization [16,17] to reduce STA. Moreover, various optimization techniques have recently been developed in other fields, and it is hoped that more actual scapular tracking methods will be invented to apply these techniques [33][34][35][36].…”

Section: Discussionmentioning

confidence: 99%

Three-Dimensional Quantitative Evaluation of the Scapular Skin Marker Movements in the Upright Posture

Yoshida

Matsumura

Yamada

et al. 2022

Sensors

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Motion capture systems using skin markers are widely used to evaluate scapular kinematics. However, soft-tissue artifact (STA) is a major limitation, and there is insufficient knowledge of the marker movements from the original locations. This study explores a scapular STA, including marker movements with shoulder elevation using upright computed tomography (CT). Ten healthy males (twenty shoulders in total) had markers attached to scapular bony landmarks and underwent upright CT in the reference and elevated positions. Marker movements were calculated and compared between markers. The bone-based and marker-based scapulothoracic rotation angles were also compared in both positions. The median marker movement distances were 30.4 mm for the acromial angle, 53.1 mm for the root of the scapular spine, and 70.0 mm for the inferior angle. Marker movements were significantly smaller on the superolateral aspect of the scapula, and superior movement was largest in the directional movement. Scapulothoracic rotation angles were significantly smaller in the marker-based rotation angles than in the bone-based rotation angles of the elevated position. We noted that the markers especially did not track the inferior movement of the scapular motion with shoulder elevation, resulting in an underestimation of the marker-based rotation angles.

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“…To validate the performance of MSACLPSO, the PSO, BLPSO, CLPSO, CPMPSO, IJAYA, GOTLBO, SATLBO, DE/BBO, DBBO, STLBO, WOA, CWOA, LWOA, GWO, EGWO, WDO, DE, JADE, and MPPCEDE [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ] algorithms were used for comparison. The parameter values of MSACLPSO were the same as given in Section 5.2 .…”

Section: Case Analysismentioning

confidence: 99%

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

Zhang

Song

2022

Entropy

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In this paper, a multi-strategy adaptive comprehensive learning particle swarm optimization algorithm is proposed by introducing the comprehensive learning, multi-population parallel, and parameter adaptation. In the proposed algorithm, a multi-population parallel strategy is designed to improve population diversity and accelerate convergence. The population particle exchange and mutation are realized to ensure information sharing among the particles. Then, the global optimal value is added to velocity update to design a new velocity update strategy for improving the local search ability. The comprehensive learning strategy is employed to construct learning samples, so as to effectively promote the information exchange and avoid falling into local extrema. By linearly changing the learning factors, a new factor adjustment strategy is developed to enhance the global search ability, and a new adaptive inertia weight-adjustment strategy based on an S-shaped decreasing function is developed to balance the search ability. Finally, some benchmark functions and the parameter optimization of photovoltaics are selected. The proposed algorithm obtains the best performance on 6 out of 10 functions. The results show that the proposed algorithm has greatly improved diversity, solution accuracy, and search ability compared with some variants of particle swarm optimization and other algorithms. It provides a more effective parameter combination for the complex engineering problem of photovoltaics, so as to improve the energy conversion efficiency.

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Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism

Cited by 146 publications

References 68 publications

Dynamic elite strategy mayfly algorithm

Dynamic elite strategy mayfly algorithm

Three-Dimensional Quantitative Evaluation of the Scapular Skin Marker Movements in the Upright Posture

A Multi-Strategy Adaptive Comprehensive Learning PSO Algorithm and Its Application

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