An <i>N</i>-State Markovian Jumping Particle Swarm Optimization Algorithm

Rahman, Izaz Ur; Wang, Zidong; Liu, Weibo; Ye, Baoliu; Zakarya, Muhammad; Liu, Xiaohui

doi:10.1109/tsmc.2019.2958550

Cited by 36 publications

(14 citation statements)

References 45 publications

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“…e enhancement of the mutation possibility boosts the aptitude and speediness of the genetic algorithm for the exploration of the global optimal solution, and the credit rating model has effectual convergence proficiency and accurate prediction accuracy. In the future, we will consider swarm evolutionary algorithms such as PSO and its various variants for integrating into the BP neural network for more robust enterprise credit rating systems [22,23]. Furthermore, large datasets should be considered to evaluate and generalize the findings of our proposed research and investigation.…”

Section: Discussionmentioning

confidence: 99%

Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

Peng

2021

Scientific Programming

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In this paper, an improved neural network enterprise credit rating model, which is grounded on a genetic algorithm, is suggested. With the characteristics of self-adaptiveness and self-learning, the genetic algorithm is utilized to adjust and enhance the thresholds and weights of the neural network connections. The potential problems of the backpropagation (BP) neural network with slothful speed of convergence and the possibility of falling into the local minimum point are solved to a convinced degree using the genetic algorithm in combination. The hybrid technique of the genetic BP neural network is applied to a credit rating system. Using commercial banks’ datasets, our experimental evaluations suggest that, using a combination of the BP neural network and the genetic algorithm, the proposed model has high accuracy in enterprise credit rating and has good application value. Moreover, the proposed model is approximately 15.9% more accurate than the classical BP neural network approach.

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Section: Discussionmentioning

confidence: 99%

Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

Peng

2021

Scientific Programming

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“…Similarly, we also carried out genetic algorithm to improve the training process of the convolutional neural network. ree evaluation metrics are used to compare the outcomes of various methods, i.e., ( 1 Scientific Programming fitness function [16]. Note that these evaluation metrics are most frequently used in the context of evolutionary optimization methods [17].…”

Section: Experimental Settingsmentioning

confidence: 99%

Quality Evaluation Method of a Mathematics Teaching Model Reform Based on an Improved Genetic Algorithm

Yang

2021

Scientific Programming

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The poor comprehensiveness of the evaluation indexes of quality evaluation methods for the traditional college mathematics teaching model reform results in low accuracy of the evaluation outcomes. In this paper, aiming at this problem, a quality evaluation method for the college mathematics teaching model reform, based on the genetic algorithm, is proposed. The simulated annealing algorithm uses the weighted comprehensive objective evaluation multiobjective optimization effect that can effectively improve the accuracy of the evaluation results. In the training process, the gradient descent back-propagation training method is used to obtain new weights for the quality evaluation of college mathematics teaching mode reforms and to score various indicators and evaluate the indicators. The mean value of the outcomes is the result of mathematics teaching quality evaluation. The experimental results show that the training error of the convolutional network of the proposed method is significantly small. Based on the genetic algorithm that improves the convolutional network training process, the obtained quality evaluation outcomes are higher in accuracy, better in goodness of fitness function, and considerably lower than other state-of-the-art methods. We observed that the improved genetic algorithm has a more than 90% goodness of fit and the error is significantly lower, that is, 0.01 to 0.04, than the classical genetic algorithm.

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“…With the purpose to improve the diversity and avoiding the premature convergence problem, an augmented PSO algorithm in combination with multiple adaptive methods has been put forward in [16] where an intelligent selection mechanism has been developed to select an appropriate search approach. Very recently, a novel N-state Markovian jumping PSO algorithm has been developed where the velocity updating equation has been adjusted based on the state evolution governed by a Markov chain [27]. It is worth pointing out that the impact of different communication topologies in the PSO algorithm has been investigated in [1].…”

Section: Development Of Pso Algorithmsmentioning

confidence: 99%

A novel randomised particle swarm optimizer

Liu

Wang

Zeng

et al. 2020

Int. J. Mach. Learn. & Cyber.

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The particle swarm optimization (PSO) algorithm is a popular evolutionary computation approach that has received an ever-increasing interest in the past decade owing to its wide application potential. Despite the many variants of the PSO algorithm with improved search ability by means of both the convergence rate and the population diversity, the local optima problem remains a major obstacle that hinders the global optima from being found. In this paper, a novel randomized particle swarm optimizer (RPSO) is proposed where the Gaussian white noise with adjustable intensity is utilized to randomly perturb the acceleration coefficients in order for the problem space to be explored more thoroughly. With this new strategy, the RPSO algorithm not only maintains the population diversity but also enhances the possibility of escaping the local optima trap. Experimental results demonstrate that the proposed RPSO algorithm outperforms some existing popular variants of PSO algorithms on a series of widely used optimization benchmark functions.

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An N-State Markovian Jumping Particle Swarm Optimization Algorithm

Cited by 36 publications

References 45 publications

Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

Research on Credit Evaluation of Financial Enterprises Based on the Genetic Backpropagation Neural Network

Quality Evaluation Method of a Mathematics Teaching Model Reform Based on an Improved Genetic Algorithm

A novel randomised particle swarm optimizer

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