scholarly journals A Joint Optimization Model of s , S Inventory and Supply Strategy Using an Improved PSO-Based Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Huayang Deng ◽  
Quan Shi ◽  
Yadong Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Lead Time ◽  
Particle Swarm Optimization Algorithm ◽  
Spare Part ◽  
Joint Optimization ◽  
Supply Network ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Joint Policy ◽  
Random Lead Time

This paper mainly discussed the problem of a multiechelon and multiperiod joint policy of inventory and supply network. According to the random lead time and customers’ inventory demand, the s , S policy was improved. Based on the multiechelon supply network and the improved, the dynasty joint model was built. The supply scheme in every period with the objective of minimum total costs is obtained. Considering the complexity of the model, the improved particle swarm optimization algorithm combining the adaptive inertia weight and grading penalty function is adopted to calculate this model and optimize the spare part problems in various environments.

Self-Adaptive Particle Swarm Optimization Algorithm with Mutation Operation Based on K-Means

2013 ◽  
Vol 760-762 ◽  
pp. 2194-2198 ◽  
Author(s):  
Xue Mei Wang ◽  
Yi Zhuo Guo ◽  
Gui Jun Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Inertia Weight ◽  
Clustering Quality ◽  
Mutation Operation

Adaptive Particle Swarm Optimization algorithm with mutation operation based on K-means is proposed in this paper, this algorithm Combined the local searching optimization ability of K-means with the gobal searching optimization ability of Particle Swarm Optimization, the algorithm self-adaptively adjusted inertia weight according to fitness variance of population. Mutation operation was peocessed for the poor performative particle in population. The results showed that the algorithm had solved the poblems of slow convergence speed of traditional Particle Swarm Optimization algorithm and easy falling into the local optimum of K-Means, and more effectively improved clustering quality.

scholarly journals A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm

PLoS ONE ◽  
2016 ◽  
Vol 11 (8) ◽  
pp. e0161558 ◽  
Author(s):  
Mohammad Javad Amoshahy ◽  
Mousa Shamsi ◽  
Mohammad Hossein Sedaaghi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Inertia Weight

An Improved Particle Swarm Optimization Algorithm

2011 ◽  
Vol 186 ◽  
pp. 454-458
Author(s):  
Hao Xiang Cheng ◽  
Jian Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Iteration Process ◽  
Concave Function ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Inertia Weight ◽  
Dynamic Adaptability

An improved particle swarm optimization (IPSO) was proposed in this paper to solve the problem that the linearly decreasing inertia weight (LDIW) of particle swarm optimization algorithm cannot adapt to the complex and nonlinear optimization process. The strategy of nonlinear decreasing inertia weight based on the concave function was used in this algorithm. The aggregation degree factor of the swarm was introduced in this new algorithm. And in each iteration process, the weight is changed dynamically based on the current aggregation degree factor and the iteration times, which provides the algorithm with dynamic adaptability. The experiments on the three classical functions show that the convergence speed of IPSO is significantly superior to LDIWPSO, and the convergence accuracy is increased.

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