Cooperative Co-evolution with Principal Component Analysis for Large Scale Optimization

Xu, Guangzhi; Zhao, Xinchao; Li, Rui

doi:10.1007/978-981-13-2829-9_39

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Mahdavi et al 11,29 comprehensively surveyed the research progress of DE and PSO in LSGO, and summarized three kinds of decomposition methods, that is, static grouping, learning‐based dynamic grouping and random dynamic grouping. Particularly, the dynamic grouping method can change the sizes of the subcomponents, in which the problem's characteristics are used to conduct the division of interacting decision variables before or during the optimization process 7,9,29,33,37 . Sun et al 33 originally developed a sufficient condition to judge whether the intersection of two unit vectors were nonempty, after which a recursive differential grouping strategy was derived to identify the interaction between two subsets of decision variables.…”

Section: Related Research Workmentioning

confidence: 99%

“…Particularly, the dynamic grouping method can change the sizes of the subcomponents, in which the problem's characteristics are used to conduct the division of interacting decision variables before or during the optimization process. 7,9,29,33,37 Sun et al 33 originally developed a sufficient condition to judge whether the intersection of two unit vectors were nonempty, after which a recursive differential grouping strategy was derived to identify the interaction between two subsets of decision variables. Kim et al 7 proposed an efficient variable interdependency identification and decomposition method, in which three core strategies, that is, binary variable space search, dynamic perturbation caching and prevariable sorting, were used to execute variable decomposition without the loss of decomposition accuracy.…”

Section: Cooperative Coevolutionary Approachmentioning

confidence: 99%

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Fly visual evolutionary neural network solving large‐scale global optimization

Zhang

Xiao

Qin

2021

Int J of Intelligent Sys

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Neurophysiologic achievements claimed that the fly visual system could naturally contribute to a type of artificial computation model which used motion‐sensitive neurons to detect the local movement direction changes of moving objects. It, however, still remains open how the neurons' information‐processing mechanisms and the inspirations of swarm intelligence can be integrated to serve an interdisciplinary topic between computer vision and intelligence optimization‐visual evolutionary neural networks. Hereby, a fly visual evolutionary neural network is developed to solve large‐scale global optimization (LSGO), inspired by swarm evolution and the characteristics of fly visual perception. It includes two functional modules, of which one is to generate global and local motion direction activities of visual neural nodes, and the other takes the activities as learning rates to update the nodes' states by a population‐like evolutionary strategy. Also, it is used to optimize the structure of a multilayer perceptron to acquire a sample classification model. The theoretical results indicate that the network is convergent and meanwhile the computational complexity mainly depends on the size of the input layer and the dimension of LSGO. The comparative experiments have verified that the network is an extremely competitive optimizer for LSGO problems.

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Section: Related Research Workmentioning

confidence: 99%

Section: Cooperative Coevolutionary Approachmentioning

confidence: 99%

Fly visual evolutionary neural network solving large‐scale global optimization

Zhang

Xiao

Qin

2021

Int J of Intelligent Sys

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“…This information is utilized in velocity vectors of particles. Because PCA is a powerful tool in dimensional reduction, it helped EAs solve high dimensional optimization problems [13,14]. Besides its application in designing search operators, local principal component analysis is used for building a regularity model in multiobjective estimation of distribution algorithms [15,16].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective differential evolution enhanced with principle component analysis for constrained optimization

Huang

et al. 2019

Swarm and Evolutionary Computation

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Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems. Many of them adopt mutation and crossover operators from differential evolution. However, these operators do not explicitly utilise features of fitness landscapes. To improve the performance of algorithms, this paper aims at designing a search operator adapting to fitness landscapes. Through an observation, we find that principle component analysis (PCA) can be used to characterise fitness landscapes. Based on this finding, a new search operator, called PCA-projection, is proposed. In order to verify the effectiveness of PCA-projection, we design two algorithms enhanced with PCA-projection for solving constrained optimization problems, called PMODE and HECO-PDE, respectively. Experiments have been conducted on the IEEE CEC 2017 competition benchmark suite in constrained optimisation. PMODE and HECO-PDE are compared with the algorithms from the IEEE CEC 2018 competition and another recent MOEA for constrained optimisation. Experimental results show that an algorithm enhanced with PCA-projection performs better than its corresponding opponent without this operator. Furthermore, HECO-PDE is ranked first on all dimensions according to the competition rules. This study reveals that decomposition-based MOEAs, such as HECO-PDE, are competitive with best single-objective and multiobjective evolutionary algorithms for constrained optimisation, but MOEAs based on non-dominance, such as PMODE, may not.

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Gradient-based fly immune visual recurrent neural network solving large-scale global optimization

Zhang

2021

Neurocomputing

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Cooperative Co-evolution with Principal Component Analysis for Large Scale Optimization

Cited by 3 publications

References 14 publications

Fly visual evolutionary neural network solving large‐scale global optimization

Fly visual evolutionary neural network solving large‐scale global optimization

Multiobjective differential evolution enhanced with principle component analysis for constrained optimization

Gradient-based fly immune visual recurrent neural network solving large-scale global optimization

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