A Novel Artificial Immune Algorithm for Solving the Job Shop Scheduling Problem

Afshari, Mohsen; Sajedi, Hedieh

doi:10.5120/7420-0464

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Given an antibody, a vaccination means modifying the genes on a number of bits in harmony with priory knowledge so as to gain better affinity value [9]. In the immune particle swarm algorithm, we make the global optimal solution to be the vaccine and the individual with the lowest affinity in swarm as the antibody to be vaccinated.…”

Section: Vaccination Strategy and Immune Selectionmentioning

confidence: 99%

Coke Ratio Prediction Based on Immune Particle Swarm Neural Networks

Yang¹,

Jin²

2015

TOCSJ

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Abstract:The clonal selection mechanism and vaccination strategy of immune system are introduced into particle swarm optimization algorithm in this paper, in order to enhance the ability of global exploration of PSO, avoiding getting into local optimum and improving the accuracy and convergence speed of BP networks. The global Cauchy mutation operator and local Gauss mutation operator are used to improve the ability of searching global optimization and the accuracy of local optimization. Then the weights and thresholds of neural networks are trained by applying the immune particle swarm optimization. Finally the coke ratio forecasting model is established based on the modified BP neural networks optimized by immune particle swarm optimizer. The result shows the forecast accuracy is more accurate than both the BP neural networks optimized by the standard PSO and the traditional BP neural networks, and provides an effective way to reduce the coke ratio and achieve energy conservation and emission reduction for iron and steel enterprise.

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Section: Vaccination Strategy and Immune Selectionmentioning

confidence: 99%