Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training

Heidari, Ali Asghar; Abbaspour, Rahim Ali; Chen, Huiling

doi:10.1016/j.asoc.2019.105521

Cited by 129 publications

(53 citation statements)

References 87 publications

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“…In order to test the performance of the proposed GBHHO, some representative algorithms were selected as comparison algorithms. These algorithms include the classic original algorithms MFO, FA [74], GWO [46], HHO, WOA and some improved algorithms ACWOA [75], OBLGWO [47], OBSCA [76], SCADE [77]. The detailed parameter values are reported in Table 2.…”

Section: Experimental Results a Benchmark Function Validationmentioning

confidence: 99%

“…The detailed parameter values are reported in Table 2. The CEC2014 [47] benchmark functions were selected as test functions for the experiment. We kept the required conditions for a fair comparison.…”

Section: Experimental Results a Benchmark Function Validationmentioning

confidence: 99%

“…There are many meta-heuristics (MAs) studied in recent works to solve a wide variety of real or artificial problems. Ones of the m ain ideas were particle swarm optimizer (PSO) [37], whale optimizer (WOA) [38]- [40], teaching-learning based optimizer (TLBO) [41], grasshopper optimizer (GOA) [42], [43], bacterial foraging optimization (BFO) [44], [45], gray wolf optimizer (GWO) [46], [47], fruit fly optimizer (FOA) [48]- [50], moth-flame optimization (MFO) [51], [52], and Harris hawks optimizer (HHO) [53]. Recently, HHO finds its applications in several types of problems [54] including micro-channel heat sink design [55], optimization of related components of harmonics polluted distribution systems [56], satellite image de-noising [57], manufacturing optimization problems [58], structural design optimization of vehicle components [59], drug design and discovery [60], and soil compression coefficient prediction [61].…”

Section: Proposed Gbhhomentioning

confidence: 99%

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Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

et al. 2020

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This study aims to propose an effective intelligent model for predicting entrepreneurial intention, which can provide a reasonable reference for the formulation of talent training programs and the guidance of entrepreneurial intention of students. The prediction model is mainly based on the kernel extreme learning machine (KELM) optimized by the improved Harris hawk's optimizer (HHO). In order to obtain better parameters and feature subsets, the Gaussian barebone (GB) strategy is introduced to improve the HHO algorithm, so as to strengthen the optimization ability for tuning parameters of KELM and identifying the compact feature subsets. Then, an optimal KELM model (GBHHO-KELM) is established according to the obtained optimal parameters and feature subsets to predict the entrepreneurial intention of students. In the experiment, GBHHO is compared with the other nine well-known methods in 30 CEC 2014 benchmark problems. The experimental findings suggest that the proposed GBHHO method is significantly superior to the existing methods in most problems. At the same time, GBHHO-KELM is compared with other machine learning methods in the prediction of entrepreneurial intention. The experimental results indicate that the proposed GBHHO-KELM can achieve better classification performance and higher stability in accordance with the four metrics. Therefore, we can conclude that the GBHHO-KELM model is expected to be an effective tool for the prediction of entrepreneurial intention.

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Section: Experimental Results a Benchmark Function Validationmentioning

confidence: 99%

Section: Experimental Results a Benchmark Function Validationmentioning

confidence: 99%

Section: Proposed Gbhhomentioning

confidence: 99%

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Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

et al. 2020

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“…Aljarah et al proposed a hybrid algorithm, which is a combination of GWO and tabu search for solving clustering‐based analysis problems. Heidari et al proposed oppositional learning‐based GWO for solving two real‐world problems of key parameter tuning of kernel extreme learning machine, and the proposed algorithm is compared with previous versions of GWO in terms of solution quality and convergence speed. Jayabarathi et al proposed a hybrid grey wolf optimizer, which is implemented by adding differential evolution operators for solving economic load dispatch problem in power systems.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

Sampangi

Jayabarathi

2020

Int Trans Electr Energ Syst

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Summary In recent years, reactive power compensation techniques are receiving more attention across the globe from both the distribution network operators and research groups due to its ability in the reduction of network power loss, voltage deviation, and enhancement of voltage stability. In this paper, two novel and robust optimization techniques implemented for solving optimal capacitor (OC) allocation problem in various distribution networks are water cycle algorithm (WCA) and grey wolf optimizer (GWO). To crisscross the feasibility, GWO and WCA techniques are tested on practical Indian 28‐bus, 47‐bus, and 52‐bus and standard 33‐bus, 69‐bus, and 85‐bus radial distribution networks with uniformly varying load levels. The simulation results obtained by WCA and GWO are compared with other techniques like inclusion and interchange variable (IIV), direct search algorithm (DSA), and teaching and learning–based optimization (TLBO) and found to be better. In addition, from the obtained results, the algorithm's efficiency is evaluated in terms of exploration and exploitation, and convergence characteristics and computational time are discussed.

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“…Based on Gaussian mutation and a chaotic local search that are employed to increase the population diversity of MFO and the flame updating process of MFO for better exploiting the locality of the solutions, respectively, the proposed CLSGMFO approach [37] is used to perform the function optimizations and is combined with a hybrid kernel extreme learning machine (KELM) model for financial prediction. Based on oppositionbased learning (OBL) and the drawbacks of GWO, OBLGWO [38] is proposed to tune the parameters of KELM for dealing with two real-world problems: second major selection (SMS) problem and thyroid cancer diagnosis (TCD) problem.…”

Section: Introductionmentioning

confidence: 99%

The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

Bai

et al. 2019

Complexity

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The antlion optimizer (ALO) is a new swarm-based metaheuristic algorithm for optimization, which mimics the hunting mechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability for some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in elitism operator of ALO is improved, and thus the improved ALO (IALO) is obtained. The proposed IALO is compared against sine cosine algorithm (SCA), PSO, Moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), and ALO by performing on 23 classic benchmark functions. The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO, and ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters of BP neural network for predicting the Chinese influenza and the predicted model is built, written as IALO-BPNN, which is against the models: BPNN, SCA-BPNN, PSO-BPNN, MFO-BPNN, MVO-BPNN, and ALO-BPNN. It is shown that the predicted model IALO-BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the weights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective and efficient algorithm suitable for optimization problems.

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Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training

Cited by 129 publications

References 87 publications

Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

Predicting Entrepreneurial Intention of Students: An Extreme Learning Machine With Gaussian Barebone Harris Hawks Optimizer

Optimal capacitor allocation in distribution networks for minimization of power loss and overall cost using water cycle algorithm and grey wolf optimizer

The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

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