Harris Hawks optimization with information exchange

Qu, Changzheng; He, Wei; Peng, Xiangni; Peng, Xiaoning

doi:10.1016/j.apm.2020.03.024

Cited by 75 publications

(42 citation statements)

References 45 publications

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“…In addition, they conducted test with four benchmark test functions and five CEC-2017 real-parameter numerical optimization problems. It was concluded that the proposed algorithm performed better than other intelligence optimization algorithms regarding the convergence rate, solution accuracy and robustness [50].…”

Section: Rosenbrockmentioning

confidence: 94%

Chaos in metaheuristic based artificial intelligence algorithms: a short review

Atali¹,

Pehlivan²,

Gürevin³

et al. 2021

Turkish Journal of Electrical Engineering and Computer Sciences

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Metaheuristic based artificial intelligence algorithms are commonly used in the solution of optimization problems. Another area -besides engineering systems-where chaos theory is widely employed is optimization problems.Being applied easily and not trapping in local optima, chaos-based search algorithms have attracted great attention. For example, it has been reported that when random number sequences generated from different chaotic systems are replaced with parameter values in bioinspired and swarm intelligence algorithms, an increase in the performance of metaheuristic algorithms is observed. Many scientific studies on developing hybrid algorithms in which metaheuristic algorithms and chaos theory are used together are already in process.In this article, scientific studies that cover the most popular metaheuristic algorithms in the literature in recent years and chaos theory subtopics together are examined. Great number of studies on metaheuristic algorithms and chaos issues exist in scientific literature. This article, hitherto, had to be limited to the most common meta-heuristic algorithms. In chaos-based metaheuristic algorithms, some advantages such as easy implementation, short application time and search acceleration have been addressed.This article is believed to contribute to two groups of researchers: The first group includes researchers already using meta-heuristic algorithms and who will come to understand that they can even improve their current techniques with chaos theory subarguments. The second group includes those who currently utilize chaotic analysis and methods in areas like nonlinear prediction modeling design and who will realize that they can make their existing methods even smarter with metaheuristic methods. Some metaheuristic algorithms use chaotic maps to solve problems such as trapping in local optimal solutions and premature convergence. In this study, such algorithms are examined through the benchmark functions they use. In addition, PSO, FA, ABC, WO algorithms are compared in terms of their common features, and algorithms with the best success rate are presented.

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Section: Rosenbrockmentioning

confidence: 94%

Chaos in metaheuristic based artificial intelligence algorithms: a short review

Atali¹,

Pehlivan²,

Gürevin³

et al. 2021

Turkish Journal of Electrical Engineering and Computer Sciences

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“…During the exploitation phase, the ∣ E ∣ is considered to choose the type of besiege to catch the prey. Accordingly, a soft one is taken when ∣ E ∣ ≥ 0.5, and the hard one is taken when ∣ E ∣ < 0.5 54–56 . This process is stimulated by the following two strategies: soft besiege and hard besiege.…”

Section: Harris Hawks Optimization Algorithmmentioning

confidence: 99%

“…Accordingly, a soft one is taken when jE j ≥ 0.5, and the hard one is taken when jE j < 0.5. [54][55][56] This process is stimulated by the following two strategies: soft besiege and hard besiege.…”

Section: Exploitation Phasementioning

confidence: 99%

Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Ismael

Qasim

Algamal

2020

Journal of Chemometrics

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Many real problems have been solved by support vector regression, especially v‐support vector regression (v‐SVR), but there are hyperparameters that usually needed to tune. In addition, v‐SVR cannot perform feature selection. Nature‐inspired algorithms have been used as a feature selection and as hyperparameters estimation procedure. In this paper, the opposition‐based learning Harris hawks optimization algorithm (HHOA‐OBL) is proposed to optimize the hyperparameters of the v‐SVR with embedding the feature selection simultaneously. The experimental results over four datasets show that the HHOA‐OBL outperforms the standard Harris hawks optimization algorithm, grid search, and cross‐validation methods, in terms of prediction, number of selected features, and running time. Besides, the experimental results of the HHOA‐OBL confirm the efficiency of the proposed algorithm in improving the prediction performance and computational time compared with other nature‐inspired algorithms, which proves the ability of HHOA‐OBL in searching for the best hyperparameters values and selecting the most informative features for prediction tasks. Thus, the experiments and comparisons support the performance of the proposed approach in making predictions in other real applications.

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“…In [9], the authors have made a brief literature review of the previous studies related to ORPD, in which different kinds of intelligent algorithms applied to the ORPD and their achievements and limitations are given. Cuckoo search (CS) algorithm [10], slime mould algorithm (SMA) [11], harmony search algorithm (HSA) [12], particle swarm optimization (PSO) algorithm [13,14], chaotic krill herd (CKH) algorithm [15], genetic algorithm (GA) [16], immune algorithm (IA) [17], earthworm optimization algorithm (EWA) [18], elephant herding optimization (EHO) [19], moth search (MS) algorithm [20], Harris hawks optimization (HHO) [21], and artificial bee colony (ABC) algorithm [22] have been proposed and used to deal with the ORPD problem. With good flexibility, versatility, and robustness, they have attracted great attention.…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization of Power System Based on Improved Differential Evolution Algorithm

Chi

et al. 2021

Mathematical Problems in Engineering

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This paper presents a novel differential evolution (DE) algorithm, with its improved version (IDE) for the benchmark functions and the optimal reactive power dispatch (ORPD) problem. Minimization of the total active power loss is usually considered as the objective function of the ORPD problem. The constraints involved are generators, transformers tapings, shunt reactors, and other reactive power sources. The aim of this study is to discover the best vector of control variables to minimize power loss, under the premise of considering the constraints system. In the proposed IDE, a new initialization strategy is developed to construct the initial population for guaranteeing its quality and simultaneously maintaining its diversity. In addition, to enhance the convergence characteristic of the original DE, two kinds of self-adaptive adjustment strategies are employed to update the scaling factor and the crossover factor, respectively, in which the detailed information about the two factors can be exchanged for each generation dynamically. Numerical applications of different cases are carried out on several benchmark functions and two standard IEEE systems, i.e., 14-bus and 30-bus test systems. The results achieved by using the proposed IDE, compared with other optimization algorithms, are discussed and analyzed in detail. The obtained results demonstrated that the proposed IDE can successfully be used to deal with the ORPD problem.

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Harris Hawks optimization with information exchange

Cited by 75 publications

References 45 publications

Chaos in metaheuristic based artificial intelligence algorithms: a short review

Chaos in metaheuristic based artificial intelligence algorithms: a short review

Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Reactive Power Optimization of Power System Based on Improved Differential Evolution Algorithm

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