Adaptive Differential Evolution With Sorting Crossover Rate for Continuous Optimization Problems

Zhou, Yinzhi; Yi, Wenchao; Gao, Liang; Li, Xinyu

doi:10.1109/tcyb.2017.2676882

Cited by 117 publications

(48 citation statements)

References 38 publications

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“…Due to the fact that the desired rhythmic compensations cannot be known a priori, an evolutionary algorithm is a preferred method for NN weight training. During the past two decades, DE has become one of the most popular evolutionary algorithm paradigms and has been successfully applied to solve numerous optimization problems in different fields [21], [34], [35]. DE has strong global search ability.…”

Section: B Desired Rhythmic Compensationsmentioning

confidence: 99%

Matsuoka’s CPG With Desired Rhythmic Signals for Adaptive Walking of Humanoid Robots

Wang

Xue

Chen

2020

IEEE Trans. Cybern.

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The desired rhythmic signals for adaptive walking of humanoid robots should have proper frequencies, phases, and shapes. Matsuoka's central pattern generator (CPG) is able to generate rhythmic signals with reasonable frequencies and phases, and thus has been widely applied to control the movements of legged robots, such as walking of humanoid robots. However, it is difficult for this kind of CPG to generate rhythmic signals with desired shapes, which limits the adaptability of walking of humanoid robots in various environments. To address this issue, a new framework that can generate desired rhythmic signals for Matsuoka's CPG is presented. The proposed framework includes three main parts. First, feature processing is conducted to transform the Matsuoka's CPG outputs into a normalized limit cycle. Second, by combining the normalized limit cycle with robot feedback as the feature inputs and setting the required learning objective, the neural network (NN) learns to generate desired rhythmic signals. Finally, in order to ensure the continuity of the desired rhythmic signals, signal filtering is applied to the outputs of NN, with the aim of smoothing the discontinuous parts. Numerical experiments on the proposed framework suggest that it can not only generate a variety of rhythmic signals with desired shapes but also preserve the frequency and phase properties of Matsuoka's CPG. In addition, the proposed framework is embedded into a control system for adaptive omnidirectional walking of humanoid robot NAO. Extensive simulation and real experiments on this control system demonstrate that the proposed framework is able to generate desired rhythmic signals for adaptive walking of NAO on fixed and changing inclined surfaces. Furthermore, the comparison studies verify that the proposed framework can significantly improve the adaptability of NAO's walking compared with the other methods.

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Section: B Desired Rhythmic Compensationsmentioning

confidence: 99%

Matsuoka’s CPG With Desired Rhythmic Signals for Adaptive Walking of Humanoid Robots

Wang

Xue

Chen

2020

IEEE Trans. Cybern.

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“…We propose a modified DE algorithm [30] with CRsort [31] and polynomial-based mutation by combining a novel CC strategy, denoted as CCDEXSPM. The work of [32] utilized the dynamic grouping method [38] into separate variables to several groups constantly and randomly.…”

Section: Modified Differential Evolution Algorithmmentioning

confidence: 99%

“…This paper comprehensively considers the coverage, lifetime, the connectivity of sensor nodes, the connectivity of cluster headers, and the reliability of fuzzy ring-based DWSNs on 3D terrain. To address it, we present a modified DE algorithm: cooperative coevolutionary (CC) [29] differential evolution (DE) algorithm [30] with CR-sort [31] and polynomial-based mutation (CCDEXSPM). In this paper, our main contributions are as follows: A.…”

Section: Introductionmentioning

confidence: 99%

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Differential Evolution-Based 3-D Directional Wireless Sensor Network Deployment Optimization

Cao

Kang

Zhao

et al. 2018

IEEE Internet Things J.

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Wireless sensor networks (WSNs) are applied more and more widely in real life. In actual scenarios, 3D directional wireless sensors (DWSs) are constantly employed, thus, research on the real-time deployment optimization problem of 3D directional wireless sensor networks (DWSNs) based on terrain big data has more practical significance. Based on this, we study the deployment optimization problem of DWSNs in the 3D terrain through comprehensive consideration of coverage, lifetime, connectivity of sensor nodes, connectivity of cluster headers and reliability of DWSNs. We propose a modified differential evolution (DE) algorithm by adopting CR-sort and polynomial-based mutation on the basis of the cooperative coevolutionary (CC) framework, and apply it to address deployment problem of 3D DWSNs. In addition, to reduce computation time, we realize implementation of message passing interface (MPI) parallelism. As is revealed by the experimentation results, the modified algorithm proposed in this paper achieves satisfying performance with respect to either optimization results or operation time.

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“…Islam et al [9] used parameter adaptation mechanism to change the parameter values of the next generation of the corresponding individuals by recording successive parameter for each generation. Zhou et al [10] introduced crossover rate sorting mechanism in which excellent individuals would be assigned a smaller crossover rate to keep good information. Thirdly, the good operator is embedded in the differential evolution algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Improved Differential Evolution Algorithm with Novel Mutation Strategy

Shen¹,

Zou²,

Zhang

2017

2017 2nd International Conference on Mechatronics and Information Technology (ICMIT 2017)

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Aiming at the defects of differential evolution, such as premature convergence, low accuracy and other shortcomings, an improved differential evolution algorithm with novel mutation strategy(NMSIDE) is presented. The novel mutation strategy is used to avoid being trapped into local minima for NMSIDE. If the evolution of the individual stagnates, the individual will rely on the current best individual to move closer to the global best individual. The mutation rate varies dynamically within the range of values, and the crossover rate is dynamically varied based on the number of iterations. NMSIDE is tested on 11 standard functions and compared with the other state-of-the-art algorithms. The experimental results show that NMSIDE has higher convergence precision, faster convergence speed and better robustness.

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Adaptive Differential Evolution With Sorting Crossover Rate for Continuous Optimization Problems

Cited by 117 publications

References 38 publications

Matsuoka’s CPG With Desired Rhythmic Signals for Adaptive Walking of Humanoid Robots

Matsuoka’s CPG With Desired Rhythmic Signals for Adaptive Walking of Humanoid Robots

Differential Evolution-Based 3-D Directional Wireless Sensor Network Deployment Optimization

An Improved Differential Evolution Algorithm with Novel Mutation Strategy

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