Fibonacci multi-modal optimization algorithm in noisy environment

Wang, Xia; Wang, Yaomin; Wu, Haifeng; Gao, Lian; Luo, Lei; Li, Peng; Shi, Xinling

doi:10.1016/j.asoc.2019.105874

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Therefore using tree-based search has the potential to improve evolutionary efficiency. In addition, tree-based searches have a strong resistance to environmental noise [29], where position of optimum point would be generated by a sampling-based distribution to enhance interference on noisy observation.…”

Section: B Search Methodsmentioning

confidence: 99%

Evolving Neural Networks through a Reverse Encoding Tree

Zhang,

Yang,

Zenil

et al. 2020

Preprint

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NeuroEvolution is one of the most competitive evolutionary learning strategies for designing novel neural networks for use in specific tasks, such as logic circuit design and digital gaming. However, the application of benchmark methods such as the NeuroEvolution of Augmenting Topologies (NEAT) remains a challenge, in terms of their computational cost and search time inefficiency. This paper advances a method which incorporates a type of topological edge coding, named Reverse Encoding Tree (RET), for evolving scalable neural networks efficiently. Using RET, two types of approaches -NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search encoding (GS-NEAT) -have been designed to solve problems in benchmark continuous learning environments such as logic gates, Cartpole, and Lunar Lander, and tested against classical NEAT and FS-NEAT as baselines. Additionally, we conduct a robustness test to evaluate the resilience of the proposed NEAT approaches. The results show that the two proposed approaches deliver an improved performance, characterized by (1) a higher accumulated reward within a finite number of time steps; (2) using fewer episodes to solve problems in targeted environments, and (3) maintaining adaptive robustness under noisy perturbations, which outperform the baselines in all tested cases. Our analysis also demonstrates that RET expends potential future research directions in dynamic environments. Code is available from https://github.com/HaolingZHANG/ReverseEncodingTree.

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Section: B Search Methodsmentioning

confidence: 99%

Evolving Neural Networks through a Reverse Encoding Tree

Zhang,

Yang,

Zenil

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The initial values is used as it covers a broader range to give the optimal result. The steps involved in the Fibonacci search algorithm are as follows [29]:…”

Section: Application Of Fibonacci Search Algorithmmentioning

confidence: 99%

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

Singh

Afshari

Liang

2023

JDMD

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Maintenance scheduling is essential and crucial for wind turbines to avoid breakdowns and reduce maintenance costs. Many maintenance models have been developed for wind turbines' maintenance planning, such as corrective, preventive and predictive maintenance. Due to communities' dependence on wind turbines for electricity needs, preventative maintenance is the most widely used method for maintenance scheduling. The downside to using this approach is that preventive maintenance is often done in fixed intervals, which is inefficient. In this paper, a more detailed maintenance plan for a 2MW wind turbine has been developed. The paper's focus is to minimize a wind turbine's maintenance cost based on a wind turbine's reliability model. This study uses a two-layer optimization framework: Fibonacci and Genetic Algorithm (GA). The first layer in the optimization method (Fibonacci) finds the optimal number of preventive maintenance required for the system. In the second layer, the optimal times for preventative maintenance and optimal components to maintain have been determined to minimize maintenance costs. The Monte Carlo simulation estimates wind turbine component failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the overall corrective and preventive maintenance costs during the system's lifetime. Finally, an optimal preventive maintenance schedule is proposed for a 2MW wind turbine using the presented method. The method used in this paper can be expanded to a wind farm or similar engineering systems.

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“…The Fibonacci method and its variations, such as Fibonacci multi-modal optimization [35] and Fibonacci tree optimization (MTO) [36], are efficient and accurate search algorithms based on Fibonacci sequences, and they conform to the following principle.…”

Section: Parameter Estimation Algorithm-multi-dimensional Fibonacci O...mentioning

confidence: 99%

Improved bond graph model for fault diagnosis under uncertain conditions

Chen

Fan

Zhong

et al. 2023

Meas. Sci. Technol.

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The effectiveness of model-based diagnosis is strongly dependent on the authenticity of the model and is influenced by various uncertainties. Measurement uncertainty is governed by the probability method and parameter uncertainty can be handled by the linear fractional transformation (LFT) while structure uncertainty and errors are rarely considered. In this paper, an improved bond graph (BG) model is proposed, which adopts subsystems to substitute modelling errors, namely parameter uncertainty and structure uncertainty. Multi-dimensional Fibonacci optimization algorithm (MFO) is developed to identify parameters in subsystems to obtain subsystem-based diagnostic hybrid bond graph (SDHBG) model. Fault diagnosis is realized by comparing the residuals and thresholds derived from the SDHBG. Experiments are conducted to validate the key concepts of the proposed methods and the results suggest the effectiveness.

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Fibonacci multi-modal optimization algorithm in noisy environment

Cited by 6 publications

References 40 publications

Evolving Neural Networks through a Reverse Encoding Tree

Evolving Neural Networks through a Reverse Encoding Tree

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

Improved bond graph model for fault diagnosis under uncertain conditions

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