Selfish herds optimization algorithm with orthogonal design and information update for training multi-layer perceptron neural network

Zhao, Ruxin; Wang, Yongli; Hu, Peng; Jelodar, Hamed; Yuan, Chi; Li, Yanchao; Masood, Isma; Rabbani, Mahdi

doi:10.1007/s10489-018-1373-1

Cited by 20 publications

(7 citation statements)

References 48 publications

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“…In [35], the authors proposed a new neural network model called multilayer perceptron with embedded feature selection (MLP-EFS). In [36], the authors proposed a selfish group optimization algorithm (OISHO) based on orthogonal design and information update. In [37], the authors proposed an improved whale optimization algorithm based on teaching and learning based on the simplex method (TSWOA).…”

Section: Related Workmentioning

confidence: 99%

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Artificial Intelligence‐Based Real‐Time Signal Sample and Analysis of Multiperson Dragon Boat Race in Complex Networks

Liu

2022

Complexity

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Dragon boat sport is a traditional activity in China. In recent years, dragon boat sport has become more and more popular around the world. In order to face more challenges, it is urgent for athletes to enhance their own strength. Scientific training methods are particularly important for athletes, and accurate training data are the basis to support scientific training. Traditional mathematical statistic methods neither can sample signals accurately nor can they do real-time analysis and feedback the characteristics to each athlete. In this paper, we use the wearable device with a triaxial accelerometer and heart rate sensor builtin to sample the speed signals and heart rate signals of athletes in various stages of men’s 1000m straight race. Based on the complex network theory, we regard the 23 dragon boat athletes in the dragon boat race as 23 nodes so as to establish a network with 23 nodes and reflect the importance of nodes by measuring the impact of node deletion on the results of the race. The neural network multilayer perceptron (MLP) model is used for training to obtain the optimal combined value with speed and heart rate for each race stage. The optimal value will be used in the simulated race as the target value to verify if it can help to improve the training efficiency. Experimental results show that the optimal value obtained by this method has a positive effect on the results of the dragon boat race which is beneficial to sports training and tactics planning.

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Section: Related Workmentioning

confidence: 99%

“…Analysis time is an important metric in MLP model construction. In this study, MLPs-EFS [35], OISHO [36], TSWOA [37], and the proposed method were used for comparison. As shown in Figure 5, the analysis time of the algorithm proposed in this paper was the shortest.…”

Section: Analysis Timementioning

confidence: 99%

Artificial Intelligence‐Based Real‐Time Signal Sample and Analysis of Multiperson Dragon Boat Race in Complex Networks

Liu

2022

Complexity

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“…Literature [ 13 ] proposed a continuous neural network model, stating that neurons can be implemented with operational amplifiers and stating that the connections of all neurons can be simulated with electronic circuits, called continuous Hopfield networks. Literature [ 14 ] provides an exhaustive analysis of the error backpropagation algorithm for multilayer feedforward networks with nonlinear continuous transfer functions, which is known as the BP algorithm. Literature [ 15 ] simulates the consumer's imagery evaluation pattern for product color matching based on BP networks and generates offspring to optimize the color matching design by genetic algorithm to speed up the design process.…”

Section: Status Of Researchmentioning

confidence: 99%

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

Wu¹

2021

Computational Intelligence and Neuroscience

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Products no longer exist simply as carriers of useful functions, but more and more consumers are beginning to pay attention to the spiritual aspects of the feelings brought by products. This paper brings machine learning algorithms to the discipline of industrial design and proposes a method to evaluate the design of product shapes using a multilayer perceptron genetic algorithm neural network (GA-MLP-NN) algorithm, quantifying the product shape, using computer-aided design technology to achieve shape optimization, shape, and color scheme generation, and using interactive feedback with users to finally generate a product shape with market demand. In this paper, we use the combinatorial innovation method to arrange and combine the detail elements in the solution library to generate the modeling solution, combine the multilayer perceptron genetic algorithm neural network algorithm with product modeling, and establish the interactive genetic modeling system for the product, use this system to design the product modeling solution, and finally get the product modeling solution satisfied by the target users; using the multilayer perceptron genetic algorithm neural network method to evaluate the product modeling items. The mapping relationship model between morphological feature space and imagery cognitive space was constructed based on multiple linear regression equations, and the multiple regression model for each affective dimension was ideal. The results show that the model performance is reliable. The weights are calculated, and the appropriate people are selected to score and calculate the modeling scheme, and finally, the satisfactory product modeling scheme is obtained.

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“…Besides, the reactor dataset was employed to test the practical applicability of the hybrid framework (Alijarah et al, 2019). Also, Zhao et al (2019) trained MLP using Modified Selfish Herd Optimization (MSHO) algorithm. It has been seen that MSHO has good exploration and convergence properties.…”

Section: A Hybrid Cpsogsa For Training Mlpmentioning

confidence: 99%

A hybrid constriction coefficient-based particle swarm optimization and gravitational search algorithm for training multi-layer perceptron

Rather

Bala

2020

IJICC

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PurposeIn this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.Design/methodology/approachIn this study, the exploration of the search space is carried out by gravitational search algorithm (GSA) and optimization of candidate solutions, i.e. exploitation is performed by particle swarm optimization (PSO). For training the multi-layer perceptron (MLP), CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error. Secondly, a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.FindingsThe experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems. Besides, it gives the best results for breast cancer, heart, sine function and sigmoid function datasets as compared to other participating algorithms. Moreover, CPSOGSA also provides very competitive results for other datasets.Originality/valueThe CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP. Basically, CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power. In the research literature, a little work is available where CPSO and GSA have been utilized for training MLP. The only related research paper was given by Mirjalili et al., in 2012. They have used standard PSO and GSA for training simple FNNs. However, the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms. In this paper, eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs. In addition, a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5% significance level to statistically validate the simulation results. Besides, eight state-of-the-art meta-heuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

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Selfish herds optimization algorithm with orthogonal design and information update for training multi-layer perceptron neural network

Cited by 20 publications

References 48 publications

Artificial Intelligence‐Based Real‐Time Signal Sample and Analysis of Multiperson Dragon Boat Race in Complex Networks

Artificial Intelligence‐Based Real‐Time Signal Sample and Analysis of Multiperson Dragon Boat Race in Complex Networks

A Product Styling Design Evaluation Method Based on Multilayer Perceptron Genetic Algorithm Neural Network Algorithm

A hybrid constriction coefficient-based particle swarm optimization and gravitational search algorithm for training multi-layer perceptron

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