Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem

Srikanth, K.; Panwar, Lokesh Kumar; Panigrahi, B. K.; Herrera‐Viedma, Enrique; Sangaiah, Arun Kumar; Wang, Gai-Ge

doi:10.1016/j.compeleceng.2017.07.023

Cited by 93 publications

(53 citation statements)

References 22 publications

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“…In addition, we hope to show the performance of DCABC by Null Hypothesis Significance Testing (NHST) [35,36] in our future work. We only test the new algorithm on classical benchmark functions and have not used it to solve practical problems, such as fault diagnosis [37], path plan [38], Knapsack [39][40][41], multi-objective optimization [42], gesture segmentation [43], unit commitment problem [44], and so on. There is an increasing interest in prompting the performance of DCABC, which will be our future research direction.…”

Section: Discussionmentioning

confidence: 99%

A Developed Artificial Bee Colony Algorithm Based on Cloud Model

Jin

Sun

2018

Mathematics

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Abstract:The Artificial Bee Colony (ABC) algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees' search formula and changing the scout bees' updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

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Section: Discussionmentioning

confidence: 99%

A Developed Artificial Bee Colony Algorithm Based on Cloud Model

Jin

Sun

2018

Mathematics

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“…In the case of a quantum approach, continuous algorithms are adapted based on the uncertainty principle, where position and velocity cannot be determined simultaneously. In [43], a quantum binary gray wolf optimizer is proposed to solve the unit commitment problem. Using a quantum binary lightning search algorithm, in [44], the optimal placement of vehicle-to-grid charging stations in the distribution power system was addressed.…”

Section: Related Binarization Workmentioning

confidence: 99%

A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem

et al. 2020

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This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.

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“…