Cosine adapted modified whale optimization algorithm for control of switched reluctance motor

Saha, Nutan; Panda, Sidhartha

doi:10.1111/coin.12310

Cited by 17 publications

(19 citation statements)

References 45 publications

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“…Experimental results show that the HWOANM provides better exploration and exploitation capabilities than other optimization algorithms and can be considered as a promising new algorithm for optimizing both design and manufacturing objectives. In [128], cosine adapted modified WOA (CAMWOA) was proposed, wherein the cosine function was incorporated for the selection of control parameter "d" governing the positions of whales during the optimization process. A multi-objective engineering problem pertaining to the control of a switched reluctance motor was used to demonstrated the efficiency of CAMWOA.…”

Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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“…Luo et al [ 37 ] improved WOA based on the regularity of chaos and the mutational character of Gaussian mutation. Saha et al [ 38 ] proposed a cosine-adapted modified whale optimization by incorporating cosine parameters into the selection of control parameters. In the study [ 1 ], LF and ranking-based mutation operators were embedded into WOA, which can prevent the algorithm from falling into local optimum and help the algorithm find the optimal solution quickly.…”

Section: Introductionmentioning

confidence: 99%

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

et al. 2022

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Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions. Second, a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA. To verify the advantages of QGBWOA, comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10, 30, 50, and 100 and on CEC 2020 test with dimension 30. Furthermore, the performance results were tested using Wilcoxon signed-rank (WS), Friedman test, and post hoc statistical tests for statistical analysis. Convergence accuracy and speed are remarkably improved, as shown by experimental results. Finally, feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems. QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics. Supplementary Information The online version contains supplementary material available at 10.1007/s42235-022-00297-8.

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“…Of late substantial research is happening for developing novel optimization techniques. A cosine-inspired modified whale optimization algorithm for speed control of motor was presented in Saha and Panda (2020). An improved moth swarm algorithm tuned hybrid fuzzy PD-PI structure is proposed in Khamari et al (2020) for frequency control of power system with electrical vehicles.…”

Section: Introductionmentioning

confidence: 99%

The performance analysis of type-2 fuzzy fractional-order tilt integral derivative controller with enhanced Harris hawks optimization

Jekan

Subramani

2021

Transactions of the Institute of Measurement and Control

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Modern Alternating Current (AC) microgrids (ACMGs) are highly complex because of the indeterminate nature of distributed energy generation and load causes frequency fluctuations. It is essential to maintain the constant power amongst load and generations by the proper design of an efficient controller to stabilize frequency fluctuations. In this paper, the type-2 fuzzy fractional-order tilt integral derivative controller structure is designed and analyzed for frequency control of an ACMG by using an enhanced Harris hawks optimization (EHHO). The modified optimization technique is analyzed employing test functions and compared with similar methods. It is noticed that the modified optimization technique is outperforming the other methods in most of the test functions. The frequency control performance of an ACMG is demonstrated and compared with the original Harris hawks optimization (HHO) and other optimization methods. The proposed type-2 fuzzy fractional-order tilt integral derivative controller tuned with EHHO technique is demonstrated to be effective and superior to any of the alternatives by application to a typical two-area test system.

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Cosine adapted modified whale optimization algorithm for control of switched reluctance motor

Cited by 17 publications

References 45 publications

A survey on several new popular swarm intelligence optimization algorithms

A survey on several new popular swarm intelligence optimization algorithms

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

The performance analysis of type-2 fuzzy fractional-order tilt integral derivative controller with enhanced Harris hawks optimization

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