Applying Improved Particle Swarm Optimization to Asynchronous Parallel Disassembly Planning

Tseng, Hwai-En; Chang, Chien-Cheng; Chung, T.

doi:10.1109/access.2022.3195863

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…As the size of components increases, Xie et al [28] proposed a modified grey wolf optimizer to obtain the optimal disassembly sequence. Tseng et al [29,30] used a Flatworm algorithm and an improved particle swarm optimization algorithm to optimize the disassembly sequence. Lou et al [31] proposed an improved multi-objective hybrid grey wolf optimization algorithm to obtain Pareto optimal disassembly plans.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process

Chen,

Li,

Chu

et al. 2024

Sustainability

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The disassembly of end-of-life (EoL) products is of high concern in sustainability research. It is important to obtain reasonable disassembly depth during the disassembly process. However, the overall safety of the disassembly process is not considered during the disassembly depth optimization process, which leads to an inability to accurately obtain a reasonable disassembly depth. Considering this, a multi-objective disassembly depth optimization method for EoL smartphones considering the overall safety of the disassembly process is proposed to accurately determine a reasonable disassembly depth in this study. The feasible disassembly depth for EoL smartphones is first determined. The reasonable disassembly process for EoL smartphones is then established. A multi-objective function for disassembly depth optimization for EoL smartphones is established based on the disassembly profit per unit time, the disassembly energy consumption per unit time and the overall safety rate of the disassembly process. In order to increase solution accuracy and avoid local optimization, an improved teaching–learning-based optimization algorithm (ITLBO) is proposed. The overall safety of the disassembly process, disassembly time, disassembly energy consumption and disassembly profit are used as the criteria for the fuzzy analytic hierarchy process (AHP) to evaluate the disassembly depth solution. A case of the ‘Xiaomi 4’ smartphone is used to verify the applicability of the proposed method. The results show that the searchability of the non-inferior solution and the optimal solution of the proposed method are improved. The convergence speeds of the ITLBO algorithm are 50.00%, 33.33% and 30.43% higher than those of the TLBO algorithm, and the optimal solution values of the ITLBO algorithm are 3.91%, 5.10% and 3.45% higher than those of the TLBO algorithm in three experiments of single objective optimization.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process

Chen,

Li,

Chu

et al. 2024

Sustainability

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“…The Particle Swarm Optimization (PSO) methodology is based on mimicking the foraging behavior of flock animals like birds using an optimization tool that outperforms the group intelligence approach. Some technical optimization issues can be solved with the PSO method [54,55,56,57], which was developed after observing this class of animal foraging patterns. All particles in the PSO algorithm have an adjustable value, and their search velocity and range are controlled by the speed at which they move.…”

Section: ) Particle Swarm Optimizationmentioning

confidence: 99%

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Hadjouni

Abdelhamid

El-kenawy³

et al. 2023

IEEE Access

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Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middleincome countries. An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth. There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images. However, these are still lacking high precision in oral cancer detection. Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy. In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN). The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER). Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%. In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach. The proposed methodology is solid and efficient, and specialists can adopt it. However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.

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“…From another perspective, it is to seek the optimal plan of integer sorting. The meta-heuristic algorithm performs excellently in the assembly sequence, such as ant colony optimization [40,41], particle swarm optimization [42][43][44], and genetic algorithm [23,35,38,45].…”

Section: The Selection and Comparison Of Optimization Algorithmmentioning

confidence: 99%

Graph Database and Matrix-Based Intelligent Generation of the Assembly Sequence of Prefabricated Building Components

Yang,

Jiang,

Dong

et al. 2023

Applied Sciences

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The assembly of prefabricated components is a critical process in prefabricated building construction, influencing both progress and accuracy. However, the assembly sequence planning and optimization (ASPO) of prefabricated components have yet to receive sufficient attention from researchers, and current research has displayed limited automation and poor generalization capabilities. Therefore, this paper proposes a framework for intelligently generating assembly sequences for prefabricated components based on graph databases and matrices. The framework utilizes an adjacency matrix and interference matrix-based modeling method to comprehensively describe the connections and constraint relationships between components, enabling better evaluation of assembly difficulty during optimization. The graph database serves as the central hub for data exchange, facilitating component information storage, automatic querying, and summarization. The obtained assembly sequence and progress plan are fed back into the graph database. To accomplish assembly sequence optimization, a genetic algorithm based on the double-elite strategy is employed. Furthermore, the effectiveness of the proposed framework is validated through an actual engineering case. The results demonstrate that the framework can effectively find an optimal assembly sequence to mitigate the assembly challenge of a prefabricated building.

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Applying Improved Particle Swarm Optimization to Asynchronous Parallel Disassembly Planning

Cited by 12 publications

References 22 publications

Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process

Multi-Objective Disassembly Depth Optimization for End-of-Life Smartphones Considering the Overall Safety of the Disassembly Process

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Graph Database and Matrix-Based Intelligent Generation of the Assembly Sequence of Prefabricated Building Components

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