Chaotic whale optimizer variants for parameters estimation of the chaotic behavior in Permanent Magnet Synchronous Motor

Yousri, Dalia; Allam, Dalia; Eteiba, Magdy B.

doi:10.1016/j.asoc.2018.10.032

Cited by 74 publications

(30 citation statements)

References 30 publications

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“…For validating the superiority of MPA statistically, the Wilcoxon signed rank test is computed to show a pairwise comparison among any two algorithms based on the following steps [46], [47]; 1) Report the maximum power values over number of runs (30) for all the considered algorithms (MPA vs PSO, MPA vs HHO, and MPA vs MRFO). 2) Compute R+ that refers to the sum of ranks for runs in which MPA shows superiority in comparison with the other counterparts (PSO, HHO, or MRFO).…”

Section: B Comparisons Among the Proposed Algorithmsmentioning

confidence: 99%

A Robust Strategy Based on Marine Predators Algorithm for Large Scale Photovoltaic Array Reconfiguration to Mitigate the Partial Shading Effect on the Performance of PV System

et al. 2020

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Large-scale solar photovoltaic (PV) plants play an essential role in providing the increasing demand for energy in recent time. Therefore, in the purpose of achieving the highest harvested power under the partial shading conditions as well as protecting the PV array from the hot-spot calamity, the PV reconfiguration strategy is established as an efficient procedure. This is performed by redistribution of PV modules according to their levels of shading. Motivated by this, the authors in this article have introduced a novel population-based algorithm that is known as marine predators algorithm (MPA) to restructure the PV array dynamically. Moreover, a novel objective function is introduced to enhance the algorithm performance rather than utilizing the regular weighted objective function in the literature. The effectiveness of the proposed algorithms based on the novel objective function is evaluated using several metrics such as fill factor, mismatch losses, percentage of power loss, and percentage of power enhancement. Besides, the obtained results are compared with a regular total-cross-tied (TCT) connection, manta ray foraging optimization (MRFO), harris hawk optimizer (HHO) and particle swarm optimizer (PSO) based reconfiguration techniques. Furthermore, to demonstrate the suitability of the proposed methods, large scale PV arrays of 16 × 16 and 25 × 25 are considered and evaluated. The results reveal that MPA enhanced the PV array power by percentage of 28.6 %, 2.7 % and 5.7 % in cases of 9 × 9, 16 × 16 and 25 × 25 PV arrays, respectively. The comprehensive comparisons endorse that MPA shows a successful shade dispersion; hence the number of multiple peaks in the PV characteristics has reduced, and high values of power have been harvested with least mean execution time in comparison with PSO, HHO and MRFO. Moreover, the Wilcoxon signed-rank test has been accomplished to confirm the reliability and applicability of the proposed approach for the PV large scale arrays as well.

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Section: B Comparisons Among the Proposed Algorithmsmentioning

confidence: 99%

A Robust Strategy Based on Marine Predators Algorithm for Large Scale Photovoltaic Array Reconfiguration to Mitigate the Partial Shading Effect on the Performance of PV System

et al. 2020

Self Cite

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“…Apart from some of the traditional methods tested, we also added five advanced algorithms, namely, SCA with differential evolution (SCADE) [63], opposition-based SCA (OBSCA) [57], fuzzy self-tuning PSO (FST_PSO) [77], chaotic SSA (CSSA) [78] and chaotic whale optimizer (CWOA) [79], to further evaluate the efficacy of the CLSCA. For comparison purposes, 13 benchmarks mentioned above (F1-F13) with two unimodal and multi-modal multimodal functions are chosen from 23 common benchmark cases.…”

Section: F Comparision With Advanced Algorithmsmentioning

confidence: 99%

Rationalized Sine Cosine Optimization With Efficient Searching Patterns

et al. 2020

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Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Lévy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Lévy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks. INDEX TERMS Sine cosine algorithm, optimization, chaotic local search, Lévy flight.

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“…Although, WOA has many advantages, slow convergence speed and falling into local optimization easily are the shortcomings [42]. In this paper, the difference operator of DE is fusion in WOA to improve the problem of falling into local optimization and balance the ability to explore and exploit.…”

Section: ) Modified Woamentioning

confidence: 99%

Short-Term Wind Speed Forecasting With Principle-Subordinate Predictor Based on Conv-LSTM and Improved BPNN

Chen

Zhang

et al. 2020

IEEE Access

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To improve the accuracy of short term wind speed forecasting, a novel principle-subordinate prediction model based on Conv-LSTM network and BPNN is designed in this paper. The proposed model combines deep learning algorithms and improved neural network to deal with the problem of wind speed forecasting. In this model, the prediction sequence of each subseries is obtained by Conv-LSTM to form more smoother and characteristic series; the BPNN, optimized by MWOA (Modified Whale Optimization Algorithm), is trained with the reconstruction sequence, which processed for the prediction sequence by invert-EMD (inverse empirical model decomposition). Before prediction, singular spectrum analysis (SSA) and complete ensemble empirical model decomposition adaptive noise (CEEMDAN) are adopted to de-noise and decompose the original wind speed data into several subseries. This process is beneficial to improve the ratio of signal to noise and simply the features of wind speed data. In addition, the Conv-LSTM is tested on three datasets, the results proved that data process is advantaged to obtain higher quality wind speed datasets, and convolutional layer can deep extract the characteristics of each subseries, it can facilitate LSTM to make accurate prediction. The final prediction results, compared with other five different models, demonstrate that the proposed model can achieve higher precision. Such as the performance evaluation matrix (MAPE = 2.62%, RMSE = 0.151) are smallest obtained from experiments on three wind different wind speed datasets. INDEX TERMS Short-term wind speed forecasting, deep learning, data mining, modified whale optimization algorithm, convolutional layer, feature extraction, principle-subordinate predictor.

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Chaotic whale optimizer variants for parameters estimation of the chaotic behavior in Permanent Magnet Synchronous Motor

Cited by 74 publications

References 30 publications

A Robust Strategy Based on Marine Predators Algorithm for Large Scale Photovoltaic Array Reconfiguration to Mitigate the Partial Shading Effect on the Performance of PV System

A Robust Strategy Based on Marine Predators Algorithm for Large Scale Photovoltaic Array Reconfiguration to Mitigate the Partial Shading Effect on the Performance of PV System

Rationalized Sine Cosine Optimization With Efficient Searching Patterns

Short-Term Wind Speed Forecasting With Principle-Subordinate Predictor Based on Conv-LSTM and Improved BPNN

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