Optimizing Berth-quay Crane Allocation considering Economic Factors Using Chaotic Quantum SSA

Xia, Cao; Yang, Zhongyi; Hong, Wei‐Chiang; Xu, Runzhang; Wang, Yutian

doi:10.1080/08839514.2022.2073719

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…The Sparrow Search Algorithm (SSA) [37] represents a heuristic optimization technique drawing inspiration from the natural foraging and social behaviors exhibited by sparrows. SSA simulates these behaviors to discover the optimal solutions.…”

Section: Sparrow Search Algorithm (Ssa)mentioning

confidence: 99%

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Yang,

Zhou,

Wang

et al. 2023

Preprint

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Some common problems, including the effect of non-detection regions on accuracy, the small size and multi-scale of defects,and the challenge of automatically optimizing neural network hyperparameters, are confronted during the metal gear end-face defect detection, lead to the inadequate performance of accuracy and efficiency, making them unsuitable for meeting the real-time online detection demands in industries. To address the problems above, this study proposes a method SF-YOLONet to detect defects on metal gear end faces by using the Optimized Evolutionary Algorithm. Firstly, a testing platform was constructed to detect surface defects on metal gear end-faces. Subsequently, in order to address the impact of non-detection regions on accuracy, this study introduces the SF algorithm, a visual saliency-based image extraction method, to eliminates interference between ineffective features in non-detection regions and edge burrs. Additionally, A network (YOLONet) for detecting end-face defects in metal gears is introduced, which integrates the CBAM module and BiFPN feature extraction strategy. These advancements enhance adaptive learning and feature extraction for small-sized defects on gear end-face, and combine low-resolution and deep-level semantic information, to detect small and multi-scale defects is enhanced. Finally, the ISSA algorithm is introduced to optimize the hyperparameters of the SF-YOLONet model, thereby avoiding the instability of manual parameterization. The experiment demonstrated that the SF-YOLONet model achieved an average precision of 98.01% and an F1 score of 0.99 on the Metal Gear end-face defect testing dataset. The average computation time for detection per image on the YOLONet model was 0.13 seconds. Compared to other deep learning models, the proposed SF-YOLONet model significantly improves precision and efficiency in detecting defects on gear end-face. This enhancement effectively meets the real-time online detection requirements of industries.

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Section: Sparrow Search Algorithm (Ssa)mentioning

confidence: 99%

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Yang,

Zhou,

Wang

et al. 2023

Preprint

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“…The laws of evolution inspire these algorithms. Metaheuristic algorithms are widely used in signal processing [14], image processing [15,16], fault detection [17], production scheduling [18], feature selection [19], path planning [20], numerical optimization [21], engineering design [22][23][24], etc.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Golden Jackal Optimization and Golden Sine Algorithm with Dynamic Lens-Imaging Learning for Global Optimization Problems

et al. 2022

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Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey’s position often depends on the male golden jackal and there is insufficient diversity of golden jackals in some cases, it is prone to falling into a local optimal optimum. In order to address these drawbacks of GJO, this paper proposes an improved algorithm, called a hybrid GJO and golden sine (S) algorithm (Gold-SA) with dynamic lens-imaging (L) learning (LSGJO). First, this paper proposes novel dual golden spiral update rules inspired by Gold-SA. These rules give GJO the ability to think like a human (Gold-SA), making the golden jackal more intelligent in the process of preying, and improving the ability and efficiency of optimization. Second, a novel nonlinear dynamic decreasing scaling factor is introduced into the lens-imaging learning operator to maintain the population diversity. The performance of LSGJO is verified through 23 classical benchmark functions and 3 complex design problems in real scenarios. The experimental results show that LSGJO converges faster and more accurately than 11 state-of-the-art optimization algorithms, the global and local search ability has improved significantly, and the proposed algorithm has shown superior performance in solving constrained problems.

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“…At the time, when the freight rate is too low [6], there will be a direct negative impact on competitors, eventually leading to vicious competition in the original oligopoly market. Although there is no possibility of vicious competition at a low freight rate in cases of short supply, high freight rates will also discourage shippers [7]. As the demand is too high, the limited transport capacity cannot ensure that the cargoes can be delivered to the destination in time.…”

Section: Introductionmentioning

confidence: 99%

Approach to an Equivalent Freight-Based Sustainable Joint-Quotation Strategy for Shipping Blockchain Alliance

Zhang

2022

Sustainability

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To improve the sustainability of the shipping industry, a practice of establishing a new type of shipping alliance based on blockchain has been implemented. In this practice, the following question emerges: How will shipping lines achieve sustainable profit improvement? This paper focuses on the freight decision-making problem by constructing a multi-round joint-quotation strategy. This paper also demonstrates the potential impact of a joint quotation strategy on the blockchain-based open freight market from a theoretical perspective. The numerical experiment results show that, compared with the initial state, the joint quotation strategy can help to stabilize shipping demands and reduce the fluctuation in overall demands. In this strategy, the freight level needs to be high to maximize profits. However, part of the demands will be sacrificed as a result. Moreover, the optimal equilibrium solution under the joint quotation strategy is relatively vulnerable to changes in the competitive relationship among the members of an alliance. In addition, the joint quotation may also be resolutely resisted by the shipper due to monopoly risk, resulting in a major risk of a sharp reduction in demand. The findings in this paper offer a decision-making reference for the sustainable development of the shipping industry.

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Optimizing Berth-quay Crane Allocation considering Economic Factors Using Chaotic Quantum SSA

Cited by 6 publications

References 29 publications

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

Research on SF-YOLONet metal gear end-face defect detection method based on evolutionary algorithm optimization

A Hybrid Golden Jackal Optimization and Golden Sine Algorithm with Dynamic Lens-Imaging Learning for Global Optimization Problems

Approach to an Equivalent Freight-Based Sustainable Joint-Quotation Strategy for Shipping Blockchain Alliance

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