Reinforcement Neural Fuzzy Surrogate-Assisted Multiobjective Evolutionary Fuzzy Systems With Robot Learning Control Application

Juang, Chia‐Feng; Bui, Trong Bac

doi:10.1109/tfuzz.2019.2907513

Cited by 41 publications

(16 citation statements)

References 47 publications

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“…The Kriging model, also known as the Gaussian process, was initially applied to the estimation of geological reserves. It was a very popular geostatistical difference method, and was further extended to the design and analysis of deterministic computer experiments [16]. The Kriging model can not only give the estimated value of the unknown function, but also the error estimate of the estimated value.…”

Section: Kriging Modelmentioning

confidence: 99%

Data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Fan¹,

Hou²,

Qu³

et al. 2021

Preprint

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The economic operation optimization of microgrid is an important research topic in the power system. Therefore, this paper proposes a surrogate model particle swarm optimization algorithm based on the global-local search mechanism. Firstly, aiming at the problem that the statistical information of Kriging model is difficult to guarantee the prediction accuracy, the dynamic transformation is carried out to enhance the robustness of the model; secondly, the global-local search mechanism is introduced to make the algorithm fully explore the fitness landscape near the Kriging model after quickly locating the current optimal particle position, so as to achieve the balance of convergence quality and convergence efficiency. The proposed method has been tested on numerous benchmark test functions from two test suites, and the results show that the proposed algorithm has more advantages than other comparison algorithms in optimization accuracy. Finally, simulations are carried out in two operating modes of microgrid islanding and grid-connected, which has verified the effectiveness of the proposed method.

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Section: Kriging Modelmentioning

confidence: 99%

Data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Fan¹,

Hou²,

Qu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Similarly, Alipour et al [63] proposed a framework in which a combination of multi-agent RL and GAs was used to solve TSPs. Recently, Juang and Bui [64] introduced a reinforcement neural fuzzy surrogate-assisted multi-objective evolutionary optimization approach for a robot-learning control application. It was applied to an ant colony optimization algorithm to improve its learning efficiency.…”

Section: Rl In Easmentioning

confidence: 99%

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

et al. 2020

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Although several multi-operator and multi-method approaches for solving optimization problems have been proposed, their performances are not consistent for a wide range of optimization problems. Also, the task of ensuring the appropriate selection of algorithms and operators may be inefficient since their designs are undertaken mainly through trial and error. This research proposes an improved optimization framework that uses the benefits of multiple algorithms, namely, a multi-operator differential evolution algorithm and a co-variance matrix adaptation evolution strategy. In the former, reinforcement learning is used to automatically choose the best differential evolution operator. To judge the performance of the proposed framework, three benchmark sets of bound-constrained optimization problems (73 problems) with 10, 30 and 50 dimensions are solved. Further, the proposed algorithm has been tested by solving optimization problems with 100 dimensions taken from CEC2014 and CEC2017 benchmark problems. A real-world application data set has also been solved. Several experiments are designed to analyze the effects of different components of the proposed framework, with the best variant compared with a number of stateof-the-art algorithms. The experimental results show that the proposed algorithm is able to outperform all the others considered.

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“…Sasaki et al designed a deep learning neural network that required 15,000 iterations to build relationships between their experimental robot's joints and the writing results [8]. In contrast to the curve-fitting approaches, many evolutionary algorithms have been applied to design robot controllers [19]- [21]. Recently, several deep neural network based methods to robotic writing were proposed [22], [23].…”

Section: Introductionmentioning

confidence: 99%

A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

Chao

Zhou

et al. 2022

IEEE Trans. Cogn. Dev. Syst.

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Reinforcement Neural Fuzzy Surrogate-Assisted Multiobjective Evolutionary Fuzzy Systems With Robot Learning Control Application

Cited by 41 publications

References 47 publications

Data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

Evolutionary Framework With Reinforcement Learning-Based Mutation Adaptation

A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

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