An enhanced binary dragonfly algorithm based on a V-shaped transfer function for optimization of pump scheduling program in water supply systems (case study of Iran)

Jafari‐Asl, Jafar; Azizyan, Gholamreza; Monfared, Seyed Arman Hashemi; Rashki, Mohsen; Andrade‐Campos, A.

doi:10.1016/j.engfailanal.2021.105323

Cited by 33 publications

(13 citation statements)

References 29 publications

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“…where s, a, c, f, and e are the weights of the five dragonfly group behaviors, respectively; ω denotes the inertia weight; and t is the number of current iterations as shown in equation (7). e dragonfly position update strategy is as follows:…”

Section: Dragonfly Algorithmmentioning

confidence: 99%

Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

Guan

Zhu

2022

Security and Communication Networks

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In order to improve the evaluation accuracy of political classroom teaching quality in universities, a DA-BP-based evaluation method of political classroom teaching quality in universities is proposed. First of all, in constructing the teaching quality evaluation system of university political courses using the framework of behavior theory, the system consists of 4 primary indicators and 15 secondary indicators. Second, the university’s 2008–2013 annual teaching quality assessment scores for the teaching quality of political courses were used as the input data of the DA-BP network, and the 2013–2016 teaching evaluation scores were used as the algorithm’s test data. In order to test the prediction accuracy of the DA-BP algorithm, it is compared with the GA-BP algorithm, PSO-BP algorithm, and BP algorithm. The research results show that DA-BP has higher prediction accuracy than other algorithms and can better evaluate the teaching quality of political courses.

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Section: Dragonfly Algorithmmentioning

confidence: 99%

Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

Guan

Zhu

2022

Security and Communication Networks

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“…These functions are common for such a task without changing the structure of the optimization algorithm. In this work, we utilized four V2-TF (V 2 = |tan(x i )|) [141] (shown in Figure 3). Each bit x j in the solution X is handled as follows:…”

Section: Mitigating Main Issues In Bho Search Algorithmmentioning

confidence: 99%

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Al-Eiadeh,

Qaddoura,

Abdallah

2024

Preprint

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High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

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“…It was tested on an adaptable IEEE 34 bus power distribution network. Jafari-Asl [99] developed a binary DA based on a new transfer function combined with an EPANET hydraulic simulation model, to reduce the energy consumption of a pumping station. The proposed model could reduce the energy costs by 27%.…”

Section: Research Progress Regarding Theory and Application Of Dragon...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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An enhanced binary dragonfly algorithm based on a V-shaped transfer function for optimization of pump scheduling program in water supply systems (case study of Iran)

Cited by 33 publications

References 29 publications

Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

Evaluation of Political Classroom Teaching Quality in Universities Based on DA-BP Algorithm

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

A survey on several new popular swarm intelligence optimization algorithms

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