EDOA: An Elastic Deformation Optimization Algorithm

Pan, Qingtao; Tang, Jun; Lao, Songyang

doi:10.1007/s10489-022-03471-x

Cited by 11 publications

(2 citation statements)

References 60 publications

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“…To assess the efficacy of our proposed algorithm, we employ it to address the optimization problems of the 23 classical benchmark functions [12] along with the latest IEEE CEC 2020 benchmark functions [37]. These benchmark functions are elaborated in detail in Tables 1 and 2.…”

Section: Resultsmentioning

confidence: 99%

MSI-HHO: Multi-Strategy Improved HHO Algorithm for Global Optimization

Wang,

Tang,

Pan

2024

Mathematics

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The Harris Hawks Optimization algorithm (HHO) is a sophisticated metaheuristic technique that draws inspiration from the hunting process of Harris hawks, which has gained attention in recent years. However, despite its promising features, the algorithm exhibits certain limitations, including the tendency to converge to local optima and a relatively slow convergence speed. In this paper, we propose the multi-strategy improved HHO algorithm (MSI-HHO) as an enhancement to the standard HHO algorithm, which adopts three strategies to improve its performance, namely, inverted S-shaped escape energy, a stochastic learning mechanism based on Gaussian mutation, and refracted opposition-based learning. At the same time, we conduct a comprehensive comparison between our proposed MSI-HHO algorithm with the standard HHO algorithm and five other well-known metaheuristic optimization algorithms. Extensive simulation experiments are conducted on both the 23 classical benchmark functions and the IEEE CEC 2020 benchmark functions. Then, the results of the non-parametric tests indicate that the MSI-HHO algorithm outperforms six other comparative algorithms at a significance level of 0.05 or greater. Additionally, the visualization analysis demonstrates the superior convergence speed and accuracy of the MSI-HHO algorithm, providing evidence of its robust performance.

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Section: Resultsmentioning

confidence: 99%

MSI-HHO: Multi-Strategy Improved HHO Algorithm for Global Optimization

Wang,

Tang,

Pan

2024

Mathematics

Self Cite

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“…To further scrutinize the simulation results, we conducted two non-parametric tests: the Wilcoxon signed-rank test and the Friedman test. The Wilcoxon signed-rank test compares the medians of two independent or paired samples, while the Friedman test compares the medians of three or more related samples [45]. These tests allow for us to ascertain if there are significant differences between the proposed algorithms and the benchmark algorithms.…”

Section: Non-parametric Testmentioning

confidence: 99%

HEDV-Greedy: An Advanced Algorithm for Influence Maximization in Hypergraphs

Wang,

Pan,

Tang

2024

Mathematics

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Influence maximization (IM) has shown wide applicability in various fields over the past few decades, e.g., viral marketing, rumor control, and prevention of infectious diseases. Nevertheless, existing research on IM primarily focuses on ordinary networks with pairwise connections between nodes, which fall short in the representation of higher-order relations. Influence maximization on hypergraphs (HIM) has received limited research attention. A novel evaluation function, which aims to evaluate the spreading influence of selected nodes on hypergraphs, i.e., expected diffusion value on hypergraph (HEDV), is proposed in this work. Then, an advanced greedy-based algorithm, termed HEDV-greedy, is proposed to select seed nodes with maximum spreading influence on the hypergraph. We conduct extensive experiments on eight real-world hypergraph datasets, benchmarking HEDV-greedy against eight state-of-the-art methods for the HIM problem. Extensive experiments conducted on real-world datasets highlight the effectiveness and efficiency of our proposed methods. The HEDV-greedy algorithm demonstrates a marked reduction in time complexity by two orders of magnitude compared to the conventional greedy method. Moreover, HEDV-greedy outperforms other state-of-the-art algorithms across all datasets. Specifically, under conditions of lower propagation probability, HEDV-greedy exhibits an average improvement in solution accuracy of 25.76%.

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Inference on process capability index $$S_{pmk}$$ for a new lifetime distribution

Karakaya

2024

Soft Comput

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In various applied disciplines, the modeling of continuous data often requires the use of flexible continuous distributions. Meeting this demand calls for the introduction of new continuous distributions that possess desirable characteristics. This paper introduces a new continuous distribution. Several estimators for estimating the unknown parameters of the new distribution are discussed and their efficiency is assessed through Monte Carlo simulations. Furthermore, the process capability index $$S_{pmk}$$ S pmk is examined when the underlying distribution is the proposed distribution. The maximum likelihood estimation of the $$S_{pmk}$$ S pmk is also studied. The asymptotic confidence interval is also constructed for $$S_{pmk}$$ S pmk . The simulation results indicate that estimators for both the unknown parameters of the new distribution and the $$S_{pmk}$$ S pmk provide reasonable results. Some practical analyses are also performed on both the new distribution and the $$S_{pmk}$$ S pmk . The results of the conducted data analysis indicate that the new distribution yields effective outcomes in modeling lifetime data in the literature. Similarly, the data analyses performed for $$S_{pmk}$$ S pmk illustrate that the new distribution can be utilized for process capability indices by quality controllers.

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EDOA: An Elastic Deformation Optimization Algorithm

Cited by 11 publications

References 60 publications

MSI-HHO: Multi-Strategy Improved HHO Algorithm for Global Optimization

MSI-HHO: Multi-Strategy Improved HHO Algorithm for Global Optimization

HEDV-Greedy: An Advanced Algorithm for Influence Maximization in Hypergraphs

Inference on process capability index $$S_{pmk}$$ for a new lifetime distribution

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