Optimizing a Neuro-Fuzzy System Based on Nature-Inspired Emperor Penguins Colony Optimization Algorithm

Harifi, Sasan; Khalilian, Madjid; Mohammadzadeh, Javad; Ebrahimnejad, Sadoullah

doi:10.1109/tfuzz.2020.2984201

Cited by 36 publications

(10 citation statements)

References 29 publications

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“…Afterward, this algorithm was applied to solve the classic inverted pendulum problem. Results demonstrated that ANFIS based on EPC provided smaller errors and better performance than other algorithms in the training and testing stages (Harifi et al, 2020).…”

Section: Recent Work Existing Applications Of Fuzzy Systemmentioning

confidence: 98%

“…They provided a case study to illustrate the advantages of fuzzy systems (Xie et al, 2020). Harifi et al (2020) proposed an ANFIS (Adaptive Neuro-Fuzzy Inference System) based on the EPC (Emperor Penguins Colony) algorithm regarding the unresolved fuzzy status in biological systems or physical systems in nature. Then, on the benchmark dataset, they compared the optimized ANFIS with other non-derivative algorithms.…”

Section: Recent Work Existing Applications Of Fuzzy Systemmentioning

confidence: 99%

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Fuzzy System Based Medical Image Processing for Brain Disease Prediction

et al. 2021

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The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.

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Section: Recent Work Existing Applications Of Fuzzy Systemmentioning

confidence: 98%

Section: Recent Work Existing Applications Of Fuzzy Systemmentioning

confidence: 99%

Fuzzy System Based Medical Image Processing for Brain Disease Prediction

et al. 2021

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“…3b. The ANFIS-GS is implemented based on the Sugeno-type FIS structure [28]. As can be seen, three rules are generated for ANFIS-GP, which lead to accurate results.…”

Section:  mentioning

confidence: 99%

Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

et al. 2020

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Prosumer microgrids (PMGs) are considered as active users in smart grids. These units are able to generate and sell electricity to aggregators or neighbor consumers in the prosumer market. Although the optimal scheduling and operation of PMGs have received a great deal of attention in recent studies, the challenges of PMG's uncertainties such as stochastic behavior of load data and weather conditions (solar irradiance, ambient temperature, and wind speed) and corresponding solutions have not been thoroughly investigated. In this paper, a new energy management systems (EMS) based on weather and load forecasting is proposed for PMG's optimal scheduling and operation. Developing a novel hybrid machine learning-based method using adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron (MLP) artificial neural network (ANN), and radial basis function (RBF) ANN to precisely predict the load and weather data is one of the most important contributions of this article. The performance of the forecasting process is improved by using a hybrid machine learning-based forecasting method instead of conventional ones. The demand response (DR) program based on the forecasted data and considering the degradation cost of the battery storage system (BSS) are other contributions. The comparison of obtained test results with those of other existing approaches illustrates that more appropriate PMG's operation cost is achievable by applying the proposed DR-based EMS using a new hybrid machine learning forecasting method.

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“…Other common swarm intelligence algorithms are ant colony optimization (ACO), which mimics the search for food and group cooperation behavior of ant colonies [25]; the whale optimization algorithm (WOA), which is inspired by the spiral bubble feeding and group collaboration of whales [26]; and the marine predator algorithm (MPA), which simulates hunting behaviors such as Brownian motion and Lévy flight of marine organisms [27]. In addition, the bat algorithm (BA) [28], cuckoo search (CS) [29], artificial bee colony (ABC) [30], the gray wolf optimization algorithm (GWO) [31], the seagull optimization algorithm (SOA) [32], the coyote optimization algorithm (COA) [33], the tern bird optimization algorithm (STOA) [34], the dolphin echolocation algorithm (DEA) [35], the krill swarm algorithm (KHA) [36], the emperor penguin optimization algorithm (EPO) [37], and the parasitic predation algorithm (PPA) [38] are some of the algorithms that have received a large amount of attention. Physics-based algorithms mainly model physical rules, more commonly using physical concepts and laws, such as the law of refraction of light, gravity, gravity and electrical circuits [39].…”

Section: Introductionmentioning

confidence: 99%

An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems

Yang

Xia

et al. 2021

Symmetry

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The transient search algorithm (TSO) is a new physics-based metaheuristic algorithm that simulates the transient behavior of switching circuits, such as inductors and capacitors, but the algorithm suffers from slow convergence and has a poor ability to circumvent local optima when solving high-dimensional complex problems. To address these drawbacks, an improved transient search algorithm (ITSO) is proposed. Three strategies are introduced to the TSO. First, a chaotic opposition learning strategy is used to generate high-quality initial populations; second, an adaptive inertia weighting strategy is used to improve the exploration ability, exploitation ability, and convergence speed; finally, a neighborhood dimensional learning strategy is used to maintain population diversity with each iteration of merit seeking. The Friedman test and Wilcoxon’s rank sum test were also used by comparing the experiments with recently popular algorithms on 18 benchmark test functions of various types. Statistical results, nonparametric sign tests, and convergence curves all indicate that ITSO develops, explores, and converges significantly better than other popular algorithms, and is a promising intelligent optimization algorithm for applications.

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Optimizing a Neuro-Fuzzy System Based on Nature-Inspired Emperor Penguins Colony Optimization Algorithm

Cited by 36 publications

References 29 publications

Fuzzy System Based Medical Image Processing for Brain Disease Prediction

Fuzzy System Based Medical Image Processing for Brain Disease Prediction

Optimal Day-Ahead Self-Scheduling and Operation of Prosumer Microgrids Using Hybrid Machine Learning-Based Weather and Load Forecasting

An Improved Transient Search Optimization with Neighborhood Dimensional Learning for Global Optimization Problems

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