Optimization of induction motors using genetic algorithms (case of Samarkand) 75

Nazarova Z.I.; Jo'rayeva Nasiba Laziz qizi; Zaripova O'.A.

doi:10.52007/ozmufti.2025.13.4.8

Article · 2025

Optimization of induction motors using genetic algorithms (case of Samarkand) 75

Edit passport — nationaldri.uz

20.1002/ozmufti/2025/v13_i4/69021553English

Passport

A mirror of the passport from the nationaldri.uz registry. It can only be edited on nationaldri.uz.

Authors

Nazarova Z.I.ORCID 0000-0002-0027-8083 (opens in a new tab)
Тошкент архитектура-қурилиш университети
Jo'rayeva Nasiba Laziz qizi ORCID 0000-0002-0027-8921 (opens in a new tab)
Toshkent arxitektura-qurilish universiteti
Zaripova O'.A.no linked profile
Toshkent arxitektura-qurilish universiteti

Abstract

The article examines optimization of induction motors using genetic algorithms (case of Samarkand) 75. A comparative analysis of international experience is provided. Practical recommendations are proposed.

Keywords

Bibliographic details

DRI: 20.1002/ozmufti/2025/v13_i4/69021553
Work type: Article
Journal: O'zMU: fan, ta'lim va innovatsiyaISSN 2181-015X
Volume and issue: Vol. 13, No. 4
Pages: 53–58
Published: 27 August 2025
Language: English
External DOI: 10.52007/ozmufti.2025.13.4.8 (opens in a new tab)
Full text: Open on the publisher's page (opens in a new tab)
Passport mirror: synced 21/09/2026, 16:51, version 1

Metrics

Citations

0

Excluding self-citations: 0

Excluding self-citations

0

Views

0

Downloads

0

Usage by source

Duplicates (24 hours) and bots are filtered out.

Usage by source
Source	Views	Downloads
Journal pagejournal.milliykengash.com	0	0
Conference proceedingsconference.milliykengash.com	0	0
DRI resolverclicks through nationaldri.uz	0	—
Total	0	0

Links

Open on nationaldri.uz (opens in a new tab)
Full passport (nationaldri.uz) (opens in a new tab)
Edit passport — nationaldri.uz (opens in a new tab)
To fix an error in the passport, the publishing journal or the author uses the nationaldri.uz account.

Monthly usage

Views and downloads over the last 24 months (all sources).

No usage statistics yet

References

Sources cited by this work and how they were matched to works in the registry.

15 entries7 matched0 under review8 external

1.
Müller K. Paxta tozalash mashinasi mustahkamlik ko'rsatkichlarini neyron tarmoqlar yordamida optimallashtirish // Xalq ta'limi. – 2012. – №5. – B. 27–112.
External source
2.
Brown A. (2013). Language learning motivation in higher education in developing countries. Journal of Chemical Education, 179, 515–963.
External source
3.
Zaripova O'.A., Tanaka H. Temir yo'l izi ishonchliligini sonli usullar bilan baholash (Navoiy viloyati masalalari misolida) // O'zbek tili va adabiyoti. – 2025. – №1. – B. 46–53.
MatchedFuzzy match, S = 1.00
4.
Schmidt P. (2020). Deep residual learning for image recognition revisited. Computers & Education, 18, 262–988. DOI: 10.1016/j.compedu.2020.830098
External source
5.
Лебедева О.М. Оптимизация температурного режима насосной станции методом математического моделирования. – М.: Наука, 1986. – 189 с.
External source
6.
Komilov Sh.G'. Geotermal energiya manbalaridan foydalanib nasos stansiyasi energiya samaradorligini oshirish (Toshkent shahri misolida): fan nomzodi (PhD) diss. avtoreferati. – Toshkent, 2022. – 71 b.
MatchedFuzzy match, S = 0.92
7.
Abdurahmonova, S. M., Nazarova, Z. I., Ibragimova, Z. A., et al. (2014). Issiqlik almashtirgich uchun nanomateriallar asosida yangi konstruksiya ishlab ciqish. FarPI ilmiy-texnika jurnali, 2(1), 1–8.
MatchedFuzzy match, S = 1.00
8.
Қодиров, О. Ж. (2016). Пахта тозалаш машинаси ҳарорат режимини сонли усуллар ёрдамида оптималлаштириш. Ин «Математик физика ва тегишли муаммолар» мавзусидаги республика илмий-амалий конференсия (пп. 11–14). Тошкент.
MatchedFuzzy match, S = 1.00
9.
Johnson M., Wang L. (2020). Language learning motivation in higher education. Agricultural Water Management, 102, 266–930. DOI: 10.1016/j.agwat.2020.915777
External source
10.
Rossi F., Nguyen T. (2019). Deep residual learning for image recognition in developing countries. Educational Psychology Review, 33, 551–976.
External source
11.
Islomova X.N. Elektr tarmog'i ishonchliligini sonli usullar bilan baholash // Til va adabiyot ta'limi. – 2022. – №7. – B. 50–61.
External source
12.
Toshmatov, V. Sh. (2017). O'zbek va arab tillarida ko'makchilarning tahlili. In «Matematik fizika va tegishli muammolar» mavzusidagi xalqaro ilmiy-amaliy konferensiya (pp. 7–9). Toshkent.
MatchedFuzzy match, S = 0.95
13.
Haydarova, Oʻ. M., Oripov, A. R., Mirzayeva, M. Sh., et al. (2018). Qayta tiklanuvchi energiya manbalaridan foydalanib kompressor qurilmasi energiya samaradorligini oshirish. Texnika yulduzlari, 19–23.
MatchedFuzzy match, S = 1.00
14.
Normatova S.I. Nasos stansiyasi energiya sarfini matematik modellashtirish yordamida optimallashtirish // TDPU: fan, ta'lim va innovatsiya. – 2017. – №1. – B. 8–20. – DRI 20.1001/tdpufti/2017/v4_i1/37165131
MatchedExact by DRI
15.
Martin C. (2010). Machine learning methods for credit scoring. Sustainability, 169, 549–957.
External source

Citing works

Works in the registry that cite this work, newest first.

This work has not been cited yet

When other works in the registry cite this work, they will appear here.

[1] 1.
Müller K. Paxta tozalash mashinasi mustahkamlik ko'rsatkichlarini neyron tarmoqlar yordamida optimallashtirish // Xalq ta'limi. – 2012. – №5. – B. 27–112.
External source

[2] 2.
Brown A. (2013). Language learning motivation in higher education in developing countries. Journal of Chemical Education, 179, 515–963.
External source

[3] 3.
Zaripova O'.A., Tanaka H. Temir yo'l izi ishonchliligini sonli usullar bilan baholash (Navoiy viloyati masalalari misolida) // O'zbek tili va adabiyoti. – 2025. – №1. – B. 46–53.
MatchedFuzzy match, S = 1.00

[4] 4.
Schmidt P. (2020). Deep residual learning for image recognition revisited. Computers & Education, 18, 262–988. DOI: 10.1016/j.compedu.2020.830098
External source

[5] 5.
Лебедева О.М. Оптимизация температурного режима насосной станции методом математического моделирования. – М.: Наука, 1986. – 189 с.
External source

[6] 6.
Komilov Sh.G'. Geotermal energiya manbalaridan foydalanib nasos stansiyasi energiya samaradorligini oshirish (Toshkent shahri misolida): fan nomzodi (PhD) diss. avtoreferati. – Toshkent, 2022. – 71 b.
MatchedFuzzy match, S = 0.92

[7] 7.
Abdurahmonova, S. M., Nazarova, Z. I., Ibragimova, Z. A., et al. (2014). Issiqlik almashtirgich uchun nanomateriallar asosida yangi konstruksiya ishlab ciqish. FarPI ilmiy-texnika jurnali, 2(1), 1–8.
MatchedFuzzy match, S = 1.00

[8] 8.
Қодиров, О. Ж. (2016). Пахта тозалаш машинаси ҳарорат режимини сонли усуллар ёрдамида оптималлаштириш. Ин «Математик физика ва тегишли муаммолар» мавзусидаги республика илмий-амалий конференсия (пп. 11–14). Тошкент.
MatchedFuzzy match, S = 1.00

[9] 9.
Johnson M., Wang L. (2020). Language learning motivation in higher education. Agricultural Water Management, 102, 266–930. DOI: 10.1016/j.agwat.2020.915777
External source

[10] 10.
Rossi F., Nguyen T. (2019). Deep residual learning for image recognition in developing countries. Educational Psychology Review, 33, 551–976.
External source

[11] 11.
Islomova X.N. Elektr tarmog'i ishonchliligini sonli usullar bilan baholash // Til va adabiyot ta'limi. – 2022. – №7. – B. 50–61.
External source

[12] 12.
Toshmatov, V. Sh. (2017). O'zbek va arab tillarida ko'makchilarning tahlili. In «Matematik fizika va tegishli muammolar» mavzusidagi xalqaro ilmiy-amaliy konferensiya (pp. 7–9). Toshkent.
MatchedFuzzy match, S = 0.95

[13] 13.
Haydarova, Oʻ. M., Oripov, A. R., Mirzayeva, M. Sh., et al. (2018). Qayta tiklanuvchi energiya manbalaridan foydalanib kompressor qurilmasi energiya samaradorligini oshirish. Texnika yulduzlari, 19–23.
MatchedFuzzy match, S = 1.00

[14] 14.
Normatova S.I. Nasos stansiyasi energiya sarfini matematik modellashtirish yordamida optimallashtirish // TDPU: fan, ta'lim va innovatsiya. – 2017. – №1. – B. 8–20. – DRI 20.1001/tdpufti/2017/v4_i1/37165131
MatchedExact by DRI

[15] 15.
Martin C. (2010). Machine learning methods for credit scoring. Sustainability, 169, 549–957.
External source