An Improved PSO Based Back Propagation Learning-MLP (IPSO-BP-MLP) for Classification

Kanungo, D. P.; Naik, Bighnaraj; Nayak, Janmenjoy; Baboo, Sarada; Behera, Himansu Sekhar

doi:10.1007/978-81-322-2205-7_32

Cited by 4 publications

(1 citation statement)

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“…e parameter optimization problem limits the classification effect of SVM in practical applications. Traditional SVM parameter optimization methods mainly include experimental methods, grid search methods, and gradient descent methods [28,29]. ese methods involve a large number of calculations and are easy to fall into local optimal solutions and can no longer meet the current requirements in terms of solving speed and accuracy.…”

Section: Parameter Optimization Of Svmmentioning

confidence: 99%

Improvement and Application of Fractional Particle Swarm Optimization Algorithm

Zhao

2022

Mathematical Problems in Engineering

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The convergence performance of existing fractional particle swarm optimization algorithm directly depends on a single fractional-order operator. When its value increases, the convergence speed of particles gets slower. When its value decreases, the probability of the particle swarm trapping into the local optimum increases. In order to solve this problem, an improved fractional particle swarm optimization (IFPSO) algorithm is proposed in this paper. New variables are introduced in this paper to redefine the formula. The fractional-order operator is added to the velocity update formula and the position update formula. The IFPSO algorithm has linearly decreasing inertia weights. Then the proposed IFPSO algorithm is used to optimize the parameters of support vector machine (SVM) and the clustering center of the K-means classifier is selected. Experimental results show that the IFPSO algorithm can effectively avoid falling into the local optimal solution. It has a faster convergence rate and better stability than the original algorithm, which proves the effectiveness of the algorithm. Examples verify that the IFPSO algorithm can improve the classification accuracy of SVM in practical application. The IFPSO algorithm effectively solves the problem that K-means algorithm is overly dependent on the initial center and may have empty classes.

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Section: Parameter Optimization Of Svmmentioning

confidence: 99%