Optimization Model for Three-State Devices Networks Reliability and Its Particle Swarm Optimization Algorithm

2012 ◽  
pp. 582-591
Author(s):  
WulanTuya ◽  
Dongkui Li ◽  
Xuebao Li ◽  
Liping Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals Path Optimization Model for Urban Transportation Networks under the Perspective of Environmental Pollution Protection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Can Liu ◽  
Zongping Li ◽  
Yueyang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Environmental Pollution ◽  
Real Time ◽  
Optimization Algorithm ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Path Optimization ◽  
Swarm Optimization ◽  
Real Time Traffic

In view of the lack of consideration of environmental protection performance in the traditional path optimization model, a path optimization model for urban traffic networks from the perspective of environmental pollution protection is proposed. Firstly, the urban real-time traffic condition is expressed by the road traffic state index, and an integer programming model is established to optimize the route with the goal of low carbon and shortest distribution time. Then, a hybrid particle swarm optimization algorithm combined with adaptive disturbance mechanism based on variable neighborhood descent is designed, which can better carry out adaptive disturbance according to the situation that the population falls into local extreme value, and the 2-opt local search method is introduced to improve the quality of solution. Finally, the improved particle swarm optimization algorithm is used to solve the two-objective model to obtain the Pareto front solution set, that is, the path scheme under real-time traffic conditions. The experimental demonstration of the proposed model based on two application scenarios shows that its distribution cost, distribution time, and carbon emission are 1975 yuan, 27 h, and 213 kg, respectively, which are better than other comparison models and have high application value.

scholarly journals An optimization model for the operations of a pallet pool with both radio-frequency identification–tagged pallets and non-tagged pallets

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401774801 ◽  
Author(s):  
Jianwei Ren ◽  
Chunhua Chen ◽  
Hao Xu ◽  
Qingqing Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Radio Frequency ◽  
Optimization Algorithm ◽  
Radio Frequency Identification ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Identification System ◽  
Swarm Optimization ◽  
Frequency Identification

In a pallet pool, pallets would be delivered through a supply chain. The operation procedure that consists of at least five operation processes as distribution, reposition, recycling, purchase (or rent), and maintenance is quite complex. These pallets are likely to be damaged, lost, destroyed, and so on. So, it is necessary to monitor the pallets using radio-frequency identification technology. However, there is no literature on the management of a pallet pool with both radio-frequency identification–tagged pallets and non-tagged pallets being put into consideration. In our research, an optimization model is presented to manage such a pallet pool. The objective of the optimization model is to minimize the total operation cost of a pallet pool including distribution cost, reposition cost, recycling cost, purchase or rent cost, loss cost, maintenance cost, loading and unloading cost, storage cost, and punishment cost. A particle swarm optimization algorithm is developed in Microsoft Visual Basic. Our numerical example shows that the optimization model and particle swarm optimization algorithm are effective. It is proved that the model and algorithm also can be used to measure whether the investment of a radio-frequency identification system is valuable or not. We proposed some suggestions for the pallet pools management.

scholarly journals Multiobjective Optimization of Cloud Manufacturing Service Composition with Improved Particle Swarm Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yongxiang Li ◽  
Xifan Yao ◽  
Min Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Service Composition ◽  
Optimization Model ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Cloud Manufacturing ◽  
Swarm Optimization ◽  
Manufacturing Service Composition

Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

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