Parallel disassembly sequence planning using improved ant colony algorithm

Yu-fei, Xing; Wu, Dongmei; Qu, Ligang

doi:10.1007/s00170-021-06753-9

Cited by 28 publications

(15 citation statements)

References 48 publications

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“…In this study, GA [3], ACO [14], and Yeh's PSO [10] are used to compare with the performance of the IPSO algorithm. The above algorithm is written in Matlab R2021a, and the computer hardware environment is an Intel (R) Core (TM) i5-7300HQ CPU @ 2.50GHz and 8.00GB memory (RAM).…”

Section: Example Testsmentioning

confidence: 99%

“…Nonetheless, the method operation is also very complicated. In addition, the multi-objective discrete artificial bee colony algorithm is used to solve the PDP problem with the goal of the shortest working time and the maximum profit [13], the genetic algorithm is used to solve the problem of aPDP [3], and the ACO method is used to solve the problem of PDP [14].…”

Section: Introductionmentioning

confidence: 99%

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Applying Improved Particle Swarm Optimization to Asynchronous Parallel Disassembly Planning

2022

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Disassembly Planning (DP) refers to an optimization method to find the most cost-effective disassembly sequence for products based on the disassembly properties of parts. In traditional sequential disassembly planning, only a single part or component is removed. To effectively improve the product disassembly efficiency, this study explores the problem of Asynchronous Parallel Disassembly Planning (aPDP) with multiple manipulators. In the aPDP situation where multiple manipulators are used, the optimization method of arranging the manipulators needs to be considered in addition to the limitation of the priority order of parts. This study proposes an improved particle swarm optimization to discuss the aPDP combination optimization problem. The minimum Make Span is the objective, and the solution status and convergence speed are compared with the results of other particle swarm optimization methods, a Genetic Algorithm and Ant Colony Optimization. The results show that the proposed improved version of the particle swarm optimization algorithm has better solution quality and execution time.INDEX TERMS Asynchronous Parallel Disassembly Planning (aPDP), Genetic algorithm (GA) , Ant Colony Optimization (ACO), Improved particle swarm optimization (IPSO)

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Section: Example Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying Improved Particle Swarm Optimization to Asynchronous Parallel Disassembly Planning

2022

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“…By improving the determination of the ant colony function, the updated user interest degree can be obtained [11,12]:…”

Section: Network Microvideo Personalizedmentioning

confidence: 99%

Simulation Path of Network Microvideo Personalized Recommendation Based on Improved Ant Colony Algorithm

Liu

2022

Mobile Information Systems

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With the rapid development of Internet technology, network information not only brings many conveniences to the majority of users but also brings about the problem of information overload. It is increasingly difficult for users to accurately obtain the information they need from the vast ocean of information. This problem has led to the research of recommendation technology, especially the simulation path of online microvideo personalized recommendations. The video recommendation model in this study adopts the idea of an ant colony algorithm. The evaluation data and browsing record data generated by users are the basis of video recommendation, and the microvideo feature model and user preference model are abstracted from the original data by improving the ant colony algorithm. The recommendation algorithm is then used to recommend online microvideos that meet users’ preferences. There are two recommendation methods, one is to find similar students through the user preference model of each student, and find the microvideo content that the students have not learned; the other is to use the user preference model and the content feature model to match the content that conforms to the user’s preference. Compared with the traditional BP neural network algorithm, GA-BP algorithm, and PSO-BP algorithm, the mean square error MSE value of the ACO algorithm for microvideo scoring classification is reduced by 11.41%, 5.93%, and 2.41%, respectively. It can be seen that the classification of video ratings by the improved ACO algorithm has lower average errors than other algorithms. The network microvideo personalized recommendation scheme designed in this study has very practical significance.

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“…Therefore, intelligent algorithms are used to optimize the sequence. The current intelligent algorithms used in disassembly sequence optimization mainly include ant colony algorithm [36][37][38], genetic algorithm [39][40][41][42], bee colony algorithm [43][44][45], particle swarm algorithm [46]. For objective disassembly, the lengths of the disassembly sequence are different.…”

Section: Disassembly Sequence Optimizationmentioning

confidence: 99%

Objective Disassembly Sequence Planning of Used Mobile Phones Based on Improved Hybrid Graph Disassembly Model

Yin

Wang

et al. 2021

Preprint

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To solve the problems of environmental pollution and waste of resources caused by used mobile phones, the study of objective disassembly sequence planning is carried out for used mobile phones. In view of the connection of mobile phone parts with multiple parts and the need to disassemble components, the concepts of containment, exclusion, and components are integrated into the hybrid graph. An improved hybrid graph is proposed and the improved hybrid graph disassembly model suitable for mobile phone disassembly is established. The ant colony algorithm is used to search for the optimal disassembly sequence, with the objective of minimum disassembly time. Finally, the improved hybrid graph disassembly model is applied to obtain the disassembly solution of HUAWEI Honor 6. The experimental results demonstrate that the disassembly sequence generated by the improved hybrid graph disassembly model can describe the actual disassembly process of disassembling components with less disassembly time.

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Parallel disassembly sequence planning using improved ant colony algorithm

Cited by 28 publications

References 48 publications

Applying Improved Particle Swarm Optimization to Asynchronous Parallel Disassembly Planning

Applying Improved Particle Swarm Optimization to Asynchronous Parallel Disassembly Planning

Simulation Path of Network Microvideo Personalized Recommendation Based on Improved Ant Colony Algorithm

Objective Disassembly Sequence Planning of Used Mobile Phones Based on Improved Hybrid Graph Disassembly Model

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