An eigenspace divide-and-conquer approach for large-scale optimization

Ren, Zhigang; Liang, Yongsheng; Wang, Muyi; Yang, Yang; Chen, An

doi:10.1016/j.asoc.2020.106911

Cited by 9 publications

(3 citation statements)

References 42 publications

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“…With a CC framework and a hybrid mutation strategy, Deng et al [52] combine quantum computing with DECC for LSOPs. Different from the decomposition in the original decision space, Ren et al [53] use singular value decomposition in the CC framework and propose an eigenspace divide-and-conquer approach.…”

Section: ) Cceasmentioning

confidence: 99%

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Wang

Jian

Zhan

et al. 2023

IEEE Trans. Evol. Computat.

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Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this paper introduces targeted modification to the certain values in the bottleneck dimensions. Analogous to gene targeting (GT) in biotechnology, we experiment on targeting the specific genes in candidate solution to improve its trait in differential evolution (DE). We propose a simple and efficient method, called GT-based DE (GTDE), to solve LSOPs. In the algorithm design, a simple GT-based modification is developed to perform on the best individual, comprising probabilistically targeting the location of bottleneck dimensions, constructing a homologous targeting vector, and inserting the targeting vector into the best individual. In this way, all the bottleneck dimensions of the best individual can be probabilistically targeted and modified to break the bottleneck and to provide global guidance for more optimal evolution. Note that the GT is only performed on the globally best individual and is just carried out as a simple operator that is added to the standard DE. Experimental studies compare the GTDE with some other state-of-the-art large-scale optimization algorithms, including the winners of CEC2010, CEC2012, CEC2013, and CEC2018 competitions on large-scale optimization. The results show that the GTDE is efficient and performs better than or at least comparable to the others in solving LSOPs.Manuscript received….revised….accepted….

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Section: ) Cceasmentioning

confidence: 99%

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Wang

Jian

Zhan

et al. 2023

IEEE Trans. Evol. Computat.

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“…This is due to the growth in dimension space, i.e., ‘‘ curse of dimensionality ” [ 4 ]. To mitigate these difficulties, researchers suggested several ideas, such as splitting the dimensionality using a divide-and-conquer scheme [ 5 ], introducing dynamic balancing between exploration and exploitation [ 6 ], or using the concept of population clustering [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Rules embedded harris hawks optimizer for large-scale optimization problems

Samma

Sama

2022

Neural Comput & Applic

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Harris Hawks Optimizer (HHO) is a recent optimizer that was successfully applied for various real-world problems. However, working under large-scale problems requires an efficient exploration/exploitation balancing scheme that helps HHO to escape from possible local optima stagnation. To achieve this objective and boost the search efficiency of HHO, this study develops embedded rules used to make adaptive switching between exploration/exploitation based on search performances. These embedded rules were formulated based on several parameters such as population status, success rate, and the number of consumed search iterations. To verify the effectiveness of these embedded rules in improving HHO performances, a total of six standard high-dimensional functions ranging from 1000-D to 10,000-D and CEC’2010 large-scale benchmark were employed in this study. In addition, the proposed Rules Embedded Harris Hawks Optimizer (REHHO) applied for one real-world high dimensional wavelength selection problem. Conducted experiments showed that these embedded rules significantly improve HHO in terms of accuracy and convergence curve. In particular, REHHO was able to achieve superior performances against HHO in all conducted benchmark problems. Besides that, results showed that faster convergence was obtained from the embedded rules. Furthermore, REHHO was able to outperform several recent and state-of-the-art optimization algorithms.

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“…Wen, et al [5], proposed an unconstrained trajectory penalty minimization model, and established its equivalence with the eigenvalue problem. Ren, et al [6], proposed an eigenspace divideand-conquer method, which proved that the method had strong robustness and good expansibility for the dimension of the problem. The eigenspace can be calculated by performing eigenvalue decomposition (EVD) on the sample covariance matrix of the data.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2021

AJAMS

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Eigenspace is a convenient way to represent sets of observations with widespread applications, so it is necessary to accurately calculate the eigenspace of data. With the advent of the era of big data, the increasing and updating of data brings great challenges to the solution of eigenspace. Hall, et al. [1], proposed that incremental method could update the eigenspace of data online, which reduces computational costs and storage space. In this paper, the updating coefficient of sample covariance matrix in incremental method is modified. Numerical analysis shows that the modified updating form has better performance.

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An eigenspace divide-and-conquer approach for large-scale optimization

Cited by 9 publications

References 42 publications

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Rules embedded harris hawks optimizer for large-scale optimization problems

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