Recent trends in bio-inspired meta-heuristic optimization techniques in control applications for electrical systems: a review

Roni, Md. Hassanul Karim; Rana, Md. Sohel; Pota, Hemanshu R.; Hasan, Md. Mahmudul; Hussain, Md. Shajid

doi:10.1007/s40435-021-00892-3

Cited by 32 publications

(14 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In such situations, stochastic techniques are a more effective alternative in comparison to deterministic calculations since they circumvent the drawbacks introduced by the application of deterministic techniques. This scenario encourages the implementation of the heuristic and meta-heuristic techniques, since they are substantially more capable of delivering greater outcomes than deterministic procedures [31], [32]. Several researchers in their research studies have adopted such similar effective techniques to identify the appropriate solution for optimum power production in order to ensure sustainable power flow through transmission corridors.…”

Section: A Literature Surveymentioning

confidence: 99%

“…The inclusion of the improvement factors aids the manta ray to traverse in small steps towards the plankton concentration while effectively searching the search space. In Equation ( 23), the expression α(x t best − x t i ) signifies the distance measured between the manta rays and the best solution in which has been modified to Equation (30) and the improved chain foraging mechanism is represented in Equation (31).…”

Section: Improved Manta Ray Foraging Optimization (Imrfo)mentioning

confidence: 99%

See 1 more Smart Citation

A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network

Paul¹,

Sinha

Bouteraa

et al. 2023

IEEE Access

View full text Add to dashboard Cite

This research manuscript proposes an Improved Manta Ray Foraging Optimization (IMRFO) algorithm for the power system congestion cost problem. The goal of the proposed Congestion Management (CM) strategy is twofold: firstly, the Generator Sensitivity Factors (GSF) is determined to select and involve the most influential power system generators that will reschedule their real power to alleviate the excess power flow in congested transmission lines. Secondly, the IMRFO has been developed and applied to attain the minimum possible congestion cost. The IMRFO has been formulated with the inclusion of correction factors in the exploration and exploitation phases to improve the coordination between these phases. The effectiveness of IMRFO has been measured considering its effective performance on the 23 conventional benchmark functions. 39 bus New England and IEEE-118 bus test system has been utilized to authenticate the effectiveness of the CM approach with the application of IMRFO. The outcomes highlight that the congestion cost achieved with IMRFO has been reduced by, of 16.08%, 13.73%, 11.78%, and 4.48 % for the 39-bus system and 14.84%, 12.97%, 9.63%, and 6.85% for 118 bus system when compared to the Bacteria Forge Optimization (BFO), Grey Wolf Optimization (GWO), Sine-Cosine Algorithm (SCA), and Original MRFO. The results gained with the implementation of IMRFO on the CM problem portrays appreciable minimization in the congestion cost, enhancement in the system voltage and losses, generates better convergence profile and computational time when contrasted with the recent optimization methods.INDEX TERMS Manta ray forge optimization, meta-heuristic technique, optimal power flow, optimization, power rescheduling, sensitivity analysis.

show abstract

Section: A Literature Surveymentioning

confidence: 99%

Section: Improved Manta Ray Foraging Optimization (Imrfo)mentioning

confidence: 99%

A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network

Paul¹,

Sinha

Bouteraa

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The Mayfly optimization Algorithm (MA) ) is a newly optimization algorithm which has already been used by researchers, to address their non-deterministic polynomial-time hardness problems (Alblehai et al, 2022;Bhattacharyya et al, 2020;Guo et al, 2021;Gupta & Gehlawat, 2022;Liu et al, 2021;Roni et al, 2022;Tamilmani et al, 2022;Zhao & Gao, 2020). The main reason behind the widely application of the particular algorithm, is that it combines major advantages of both evolutionary and swarm intelligence optimization algorithms.…”

Section: Mayfly Optimization Algorithmmentioning

confidence: 99%

A bio-inspired computational classifier system for the evaluation of children’s theatrical anxiety at school

et al. 2023

View full text Add to dashboard Cite

Theatrical performance constitutes a complicated way for students to express and to communicate with each other, since it targets both various artistic and educational goals. Even though it constitutes a top moment of students’ expression, several students do not feel comfortable when participating in such cultural activities, as performance anxiety, a negative emotional experience stemming from the public audience exposure, affects them. The aim of this research is to apply and evaluate a student segmentation technique with the help of bio-inspired computational intelligence, for identifying high levels of performance anxiety at schoolchildren. A Mayfly-based clustering optimization algorithm is applied on a dataset with 774 instances of students to classify them according to their levels of emotions and performance anxiety that are developed during the event. A comparison with a genetic algorithm as well as particle swarm optimization shows that the proposed method is distinguished by superior categorization capabilities. The findings demonstrate the effective dissimilar student groups formation, with the members of each being distinguished by similar characteristics in terms of emotions and performance anxiety, highlighting the ones with unmanageable emotional experiences. Therefore, the drama educator is able to effortlessly detect, manage students and develop coping practices in those at risk, by acknowledging each group’s characteristics.

show abstract

“…Regardless of which model is used, the model parameters are usually identified using meta-heuristic optimization techniques [ 3 ]. Deterministic algorithms such as the non-linear least square method were conventionally employed in bio-impedance parameter extraction problems but showed no robustness against noise [ 23 ] and tended to fail as the problem size grew larger [ 24 ]. Recently, meta-heuristic optimization algorithms were used to overcome these shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Plant Tissue Modelling Using Power-Law Filters

Gadallah

Ghoneim

Elwakil

et al. 2022

Sensors

View full text Add to dashboard Cite

Impedance spectroscopy has became an essential non-invasive tool for quality assessment measurements of the biochemical and biophysical changes in plant tissues. The electrical behaviour of biological tissues can be captured by fitting its bio-impedance data to a suitable circuit model. This paper investigates the use of power-law filters in circuit modelling of bio-impedance. The proposed models are fitted to experimental data obtained from eight different fruit types using a meta-heuristic optimization method (the Water Cycle Algorithm (WCA)). Impedance measurements are obtained using a Biologic SP150 electrochemical station, and the percentage error between the actual impedance and the fitted models’ impedance are reported. It is found that a circuit model consisting of a combination of two second-order power-law low-pass filters shows the least fitting error.

show abstract

Recent trends in bio-inspired meta-heuristic optimization techniques in control applications for electrical systems: a review

Cited by 32 publications

References 75 publications

A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network

A Novel Improved Manta Ray Foraging Optimization Approach for Mitigating Power System Congestion in Transmission Network

A bio-inspired computational classifier system for the evaluation of children’s theatrical anxiety at school

Plant Tissue Modelling Using Power-Law Filters

Contact Info

Product

Resources

About