Adaptive online data-driven closed-loop parameter control strategy for swarm intelligence algorithm

Lu, Hui; Liu, Yaxian; Cheng, Shi; Shi, Yuhui

doi:10.1016/j.ins.2020.05.016

Cited by 13 publications

(2 citation statements)

References 41 publications

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“…The algorithm designers set the values of the parameters in the offline methods before running the algorithm, and the values remain unchanged during the search process, whereas in the online parameter setting, the values of the parameters are adjusted in real-time. Concerning the philosophy of adopted online parameter settings, the DE variants can be classified into three classes: deterministic, adaptive, and self-adaptive (Eiben and Smith, 2015;Lu et al, 2020;Maučec and Brest, 2019). Some DE algorithms utilize deterministic rules to set the parameter values without getting any feedback (Eiben and Smith, 2015;Storn and Price, 1997), while SaDE (Qin and Suganthan, 2005), jDE (Brest et al, 2006), ADE (dos Santos Coelho et al, 2013), and SaNSDE (Yang et al, 2008) dynamically adapt the new values by getting feedback from the search process.…”

Section: Parame Te Rs Se Ttingmentioning

confidence: 99%

QANA: Quantum-based avian navigation optimizer algorithm

Zamani

Nadimi-Shahraki

Gandomi

2021

Engineering Applications of Artificial Intelligence

170

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Section: Parame Te Rs Se Ttingmentioning

confidence: 99%

QANA: Quantum-based avian navigation optimizer algorithm

Zamani

Nadimi-Shahraki

Gandomi

2021

Engineering Applications of Artificial Intelligence

170

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“…Many real-world optimization problems involve more than two conflicting objectives to be optimized simultaneously [1][2][3][4]. Formally, these problems can be formulated as…”

Section: Introductionmentioning

confidence: 99%

A Surrogate-Assisted Many-Objective Evolutionary Algorithm Using Multi- Classification and Coevolution for Expensive Optimization Problems

et al. 2021

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Surrogate-assisted evolutionary algorithms have received a surge of attentions for their promising ability of solving expensive optimization problems. Existing surrogate-assisted evolutionary algorithms usually adopt the regression models and the binary classification models to guide the evolution of the population for solving the multiobjective optimization problems. However, the regression models will make the algorithm to be increasingly computation-expensive as the number of objectives increases, while the use of the binary classification models might suffer from the poor diversity since the diversified information of solutions cannot be reflected in these classification models. For this issue, this paper proposes a surrogate-assisted many-objective evolutionary algorithm using the cooperation of the multiclassification and regression models to improve the search quality while reducing the computational cost. Our approach includes two parts: At the model training stage, a multi-classification model is constructed to divide the whole population into several classes for ensuring diversity, a distance regression model and an angle regression model are used to select solutions with better convergence and diversity in each class; At the evolution stage, a coevolutionary framework is used to guide the evolution according to a new selection criterion. Experimental results verify the effectiveness of the proposed algorithm on a set of expensive test problems with up to 10 objectives.

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