Neighborhood centroid opposite-based learning Harris Hawks optimization for training neural networks

Fan, Cheng-Qi; Zhou, Yongquan; Tang, Zhonghua

doi:10.1007/s12065-020-00465-x

Cited by 17 publications

(5 citation statements)

References 44 publications

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“…They applied the OHHO in feature selection in breast cancer classification. Fan et al [113] proposed a novel HHO version called NCOHHO, which improves the HHO by two mechanisms: neighborhood centroid and opposite-based learning. In NCOHHO, neighborhood centroid is considered a reference point in generating the opposite particle.…”

Section: Opposite Hhomentioning

confidence: 99%

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Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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Section: Opposite Hhomentioning

confidence: 99%

“…Fan et al [113] used their novel algorithm, NCOHHO, in training a multilayer feedforward neural network using five different datasets. A similar work which combined the HHO with ANN was conducted by Moayedi et al [236] and applied to predict the compression coefficient of soil.…”

Section: Unit Commitment Problemmentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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“…The algorithm has a simple principle, fewer parameters, and better global search capability. Therefore, HHO has been applied in image segmentation [48], neural network training [49], motor control [50] and other fields. However, similar to other swarm intelligence optimization algorithms, HHO has the disadvantages of slow convergence speed, low optimization accuracy, and easily falls into local optimum when solving complex optimization problems.…”

Section: Of 41mentioning

confidence: 99%

Harris Hawks Optimization with Multi-Strategy Search and Application

et al. 2021

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The probability of the basic HHO algorithm in choosing different search methods is symmetric: about 0.5 in the interval from 0 to 1. The optimal solution from the previous iteration of the algorithm affects the current solution, the search for prey in a linear way led to a single search result, and the overall number of updates of the optimal position was low. These factors limit Harris Hawks optimization algorithm. For example, an ease of falling into a local optimum and the efficiency of convergence is low. Inspired by the prey hunting behavior of Harris’s hawk, a multi-strategy search Harris Hawks optimization algorithm is proposed, and the least squares support vector machine (LSSVM) optimized by the proposed algorithm was used to model the reactive power output of the synchronous condenser. Firstly, we select the best Gauss chaotic mapping method from seven commonly used chaotic mapping population initialization methods to improve the accuracy. Secondly, the optimal neighborhood perturbation mechanism is introduced to avoid premature maturity of the algorithm. Simultaneously, the adaptive weight and variable spiral search strategy are designed to simulate the prey hunting behavior of Harris hawk to improve the convergence speed of the improved algorithm and enhance the global search ability of the improved algorithm. A numerical experiment is tested with the classical 23 test functions and the CEC2017 test function set. The results show that the proposed algorithm outperforms the Harris Hawks optimization algorithm and other intelligent optimization algorithms in terms of convergence speed, solution accuracy and robustness, and the model of synchronous condenser reactive power output established by the improved algorithm optimized LSSVM has good accuracy and generalization ability.

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“…Feed-forward network [19], Radial basis function (RBF) network [20], Recurrent neural network [21], and convolutional neural network(CNN) [22]. The FNN is the most popular among them due to its straightforward design and effective functionality [23][24][25]. ANN have a high level of performance and simple to implement, and they can capture the hidden relationship between the inputs.…”

Section: Introductionmentioning

confidence: 99%

A modified weighted chimp optimization algorithm for training feed-forward neural network

Atta

Ali²,

Elshamy³

2023

PLoS ONE

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Swarm intelligence algorithms (SI) have an excellent ability to search for the optimal solution and they are applying two mechanisms during the search. The first mechanism is exploration, to explore a vast area in the search space, and when they found a promising area they switch from the exploration to the exploitation mechanism. A good SI algorithm can balance the exploration and the exploitation mechanism. In this paper, we propose a modified version of the chimp optimization algorithm (ChOA) to train a feed-forward neural network (FNN). The proposed algorithm is called a modified weighted chimp optimization algorithm (MWChOA). The main drawback of the standard ChOA and the weighted chimp optimization algorithm (WChOA) is they can be trapped in local optima because most of the solutions update their positions based on the position of the four leader solutions in the population. In the proposed algorithm, we reduced the number of leader solutions from four to three, and we found that reducing the number of leader solutions enhances the search and increases the exploration phase in the proposed algorithm, and avoids trapping in local optima. We test the proposed algorithm on the Eleven dataset and compare it against 16 SI algorithms. The results show that the proposed algorithm can achieve success to train the FNN when compare to the other SI algorithms.

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Neighborhood centroid opposite-based learning Harris Hawks optimization for training neural networks

Cited by 17 publications

References 44 publications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Harris Hawks Optimization with Multi-Strategy Search and Application

A modified weighted chimp optimization algorithm for training feed-forward neural network

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