Recent Advances in Swarm Intelligence and Evolutionary Computation

Yang, Xin‐She

doi:10.1007/978-3-319-13826-8

Cited by 36 publications

(7 citation statements)

References 32 publications

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“…It is also possible to combine only deterministic methods [34] or only stochastic methods. Hybrid algorithms can be divided due to their structure and principle of operation: cooperating and integrated [56].…”

Section: Methodology Of Selecting Optimal Pid Controller Parameters 3...mentioning

confidence: 99%

Comparable analysis of PID controller settings in order to ensure reliable operation of active foil bearings

Witanowski¹,

Breńkacz²,

Szewczuk-Krypa³

et al. 2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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In comparison to the traditional solutions, active bearings offer great operating flexibility, ensure better operating conditions over a wider range of rotational speeds and are safe to use. In order to ensure optimum bearing performance a bearing control system is used that adapts different geometries during device operation. The selection of optimal controller parameters requires the use of modern optimization methods that make it possible to quickly achieve the assumed parameters. This article presents the method that has been employed to select the parameters of a proportional integral derivative (PID) controller, in which both stochastic algorithms and hybrid methods have been compared. The results show that all of the used algorithms were able to reach the global optimum but only the hybrid algorithm was repeatable in all runs within a low value of the standard deviation. The best solution will be proposed in the future to control an active foil bearing. Analysing of this paper would help to prevent failures of active foil bearing used in the designed rotating machine.

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Section: Methodology Of Selecting Optimal Pid Controller Parameters 3...mentioning

confidence: 99%

Comparable analysis of PID controller settings in order to ensure reliable operation of active foil bearings

Witanowski¹,

Breńkacz²,

Szewczuk-Krypa³

et al. 2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…Literature in recent times [11,23,71] provides an outlook on the widespread applications of metaheuristic-based stochastic optimizers in multimedia tools and artificial intelligence (AI). The adaption of meta-heuristic solvers coupled with IoT [58] in big-data data analytics [52], blockchain [11], video games [10], artificial intelligence [24], feature selection [15], machine learning [14,53], and deep learning has gained immense popularity on account of its simplicity, accuracy in training and testing and robustness to entrapment [7,58].…”

Section: A Review Of Meta-heuristics In Multimedia Applications and A...mentioning

confidence: 99%

A competitive learning-based Grey wolf Optimizer for engineering problems and its application to multi-layer perceptron training

Reddy

Narayana

2023

Multimed Tools Appl

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This article presents a competitive learning-based Grey Wolf Optimizer (Clb-GWO) formulated through the introduction of competitive learning strategies to achieve a better trade-off between exploration and exploitation while promoting population diversity through the design of difference vectors. The proposed method integrates population sub-division into majority groups and minority groups with a dual search system arranged in a selective complementary manner. The proposed Clb-GWO is tested and validated through the recent CEC2020 and CEC2019 benchmarking suites followed by the optimal training of multi-layer perceptron’s (MLPs) with five classification datasets and three function approximation datasets. Clb-GWO is compared against the standard version of GWO, five of its latest variants and two modern meta-heuristics. The benchmarking results and the MLP training results demonstrate the robustness of Clb-GWO. The proposed method performed competitively compared to all its competitors with statistically significant performance for the benchmarking tests. The performance of Clb-GWO the classification datasets and the function approximation datasets was excellent with lower error rates and least standard deviation rates.

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“…Another methodology approached is the hybridization of two existing meta-heuristics wherein a solid meta-heuristic immune to the negative aspects of the parent algorithms and benefitting from the reliable aspects is worked up [9]. Hybridization promotes the collective collaboration of both the meta-heuristics since the advantageous aspects are consolidated to such an extent that the different search mechanisms help each other in dynamically exploring while exploiting the search space [10].…”

Section: B Improvement Techniquesmentioning

confidence: 99%

Symbiotic Learning Grey Wolf Optimizer for Engineering and Power Flow Optimization Problems

Reddy

Narayana

2022

IEEE Access

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This article presents a symbiotic learning-based Grey Wolf Optimizer (SL-GWO) formulated through the introduction of symbiotic hunting and learning strategies to achieve a better trade-off between exploration and exploitation while standing immune to the curse of dimensionality. The proposed method improves the performance of the algorithm to effectively handle problems with larger dimensions while avoiding local entrapment, accelerates convergence, and improves the precision and accuracy of exploitation. SL-GWO's symbiotic hunting strategies provide a major overhaul to the exiting hierarchical hunting through population sub-grouping into attacking hunters and experienced hunters with individually crafted dynamic adaptive tuning. The hunting mechanisms are implemented through the inclusion of random omega wolves from the wolfpack thereby reducing the algorithm's excessive dependence on the three dominant wolves and enhancing the population diversity. SL-GWO is tested and validated through a series of benchmarking, engineering and real-world optimization problems and compared against the standard version of GWO, eight of its latest and state-of-the-art variants and five modern meta-heuristics. Different testing scenarios are considered to analyze and evaluate the performance of the proposed method such as the effect of dimensionality (CEC2018 benchmarking suite), convergence speeds, avoidance of local entrapment (CEC2019 benchmarking suite) and constrained optimization problems (four standard engineering problems). Furthermore, two power flow problems namely, the optimal power flow (13 cases for IEEE 30 and 57-bus system) and optimal reactive power dispatch (8 cases for IEEE 30 and 57-bus system) from the recent literature are investigated. The proposed method performed competitively compared to all its competitors with statistically significant performance while requiring lower computational times. The performance for the standard engineering problems and the power flow problems was excellent with good accuracy of the solutions and the least standard deviation rates.

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Recent Advances in Swarm Intelligence and Evolutionary Computation

Cited by 36 publications

References 32 publications

Comparable analysis of PID controller settings in order to ensure reliable operation of active foil bearings

Comparable analysis of PID controller settings in order to ensure reliable operation of active foil bearings

A competitive learning-based Grey wolf Optimizer for engineering problems and its application to multi-layer perceptron training

Symbiotic Learning Grey Wolf Optimizer for Engineering and Power Flow Optimization Problems

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