Design of spark ignition engine speed control using bat algorithm

Setiadi, Herlambang; Jones, Karl O.; Nugroho, Teguh Aryo; Abdillah, Muhammad; Trilaksana, Herri; Amrillah, Tahta

doi:10.11591/ijece.v11i1.pp794-801

Cited by 7 publications

(6 citation statements)

References 19 publications

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“…11 ), most of the BA applications under electrical and power system research domain. In this domain, the optimization problems addressed by BA are economic/emission dispatch [ 40 , 173 , 267 , 292 ], power economic dispatch [ 210 ], reactive power dispatch [ 209 , 214 ], optimal power flow [ 326 ], electronics, control, and communication [ 1 , 38 , 132 , 226 , 257 , 266 ], solar PV parameter estimation and solar radiation forecasting [ 3 , 310 , 337 ], photovoltaic energy system [ 76 , 94 – 96 , 145 , 176 , 194 , 225 , 259 ], forecasting of petroleum consumption [ 58 ], load frequency control [ 19 , 92 , 93 ], energy consumption [ 104 , 219 , 261 ], proportional-integral-derivative (PID) controller [ 60 , 122 , 164 , 187 , 188 , 193 , 224 , 233 , 330 ], wind speed forecasting [ 67 , 174 , 222 , 309 , 311 ], power system [ 30 , 31 , 36 , 70 , 81 , 91 , 106 , 121 , 162 , 258 , 278 , 284 ], dam–reservoir operation [ 99 ], and others [ 136 ]. These applications are presented in Fig.…”

Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

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Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

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“…The bat algorithm (BA) was introduced by Yang [171] in 2010 as a way for searching for global optimal solutions. BA has gained increased attention due to its simplicity, a small number of parameters, high robustness, and ease of implementation [144], [172]- [177]. The BA is designed to replicate bats echolocation activity.…”

Section: Bat Algorithmmentioning

confidence: 99%

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

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The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.

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“…In this condition, the circuit and the firefly algorithm were designed and coded in Simulink and Matlab. The objective function of FA was also used to reduce the error of the boost converter's voltage output, with the fitness function mathematically described using (23) [32], [33]. Table 2 shows the parameter of FA for optimizing the PI controller.…”

Section: Design Conceptmentioning

confidence: 99%

Intelligent voltage controller based on firefly algorithm for DC-DC boost converter

Darmansyah

Robandi

2022

IJEECS

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DC-DC boost converter is one of the important tools of photovoltaic (PV) power plant. DC-DC boost converter can be used to adjust the voltage output of photovoltaic before going to the inverter. In addition, DC-DC boost converter can be used as additional devices to make the PV plant operated in maximum power point condition. To get the optimal voltage adjusment, an appropriate controller is essential. Generally, PI controller is commonly used to control the switching procedure of the boost converter. However, with uncertainty of the source (uncertainty of the PV output), PI controller is out off dated. Hence designing PI controller based on the metaheuristic algorithm such as firefly algorithm is essential. This paper is proposed an intelligent voltage controller for DC-DC boost converter based on firefly algorithm (FA). From the simulation results it is noticeable that the PI based on FA could provide better control signal (indicated by the fastest settling time of the boost converter).

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Design of spark ignition engine speed control using bat algorithm

Cited by 7 publications

References 19 publications

Recent advances of bat-inspired algorithm, its versions and applications

Recent advances of bat-inspired algorithm, its versions and applications

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Intelligent voltage controller based on firefly algorithm for DC-DC boost converter

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