The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study
A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in add...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/1/28 |
| _version_ | 1797499275595218944 |
|---|---|
| author | Ismael Jannoud Yousef Jaradat Mohammad Z. Masoud Ahmad Manasrah Mohammad Alia |
| author_facet | Ismael Jannoud Yousef Jaradat Mohammad Z. Masoud Ahmad Manasrah Mohammad Alia |
| author_sort | Ismael Jannoud |
| collection | DOAJ |
| description | A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>25.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> and data throughput by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>26.86</mn><mo>%</mo></mrow></semantics></math></inline-formula>. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round. |
| first_indexed | 2024-03-10T03:45:08Z |
| format | Article |
| id | doaj.art-1ea8a582b2744e89839900183517bec0 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T03:45:08Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-1ea8a582b2744e89839900183517bec02023-11-23T11:21:45ZengMDPI AGElectronics2079-92922021-12-011112810.3390/electronics11010028The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative StudyIsmael Jannoud0Yousef Jaradat1Mohammad Z. Masoud2Ahmad Manasrah3Mohammad Alia4Electrical Engineering Department, Al-Zaytoonah University of Jordan, Amman 11733, JordanElectrical Engineering Department, Al-Zaytoonah University of Jordan, Amman 11733, JordanElectrical Engineering Department, Al-Zaytoonah University of Jordan, Amman 11733, JordanMechanical Engineering Department, Al-Zaytoonah University of Jordan, Amman 11733, JordanComputer Science Department, Al-Zaytoonah University of Jordan, Amman 11733, JordanA genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>25.8</mn><mo>%</mo></mrow></semantics></math></inline-formula> and data throughput by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>26.86</mn><mo>%</mo></mrow></semantics></math></inline-formula>. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.https://www.mdpi.com/2079-9292/11/1/28genetic algorithmroulette wheelexponential selectionrank selectiontournament selectionstochastic selection |
| spellingShingle | Ismael Jannoud Yousef Jaradat Mohammad Z. Masoud Ahmad Manasrah Mohammad Alia The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study Electronics genetic algorithm roulette wheel exponential selection rank selection tournament selection stochastic selection |
| title | The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study |
| title_full | The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study |
| title_fullStr | The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study |
| title_full_unstemmed | The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study |
| title_short | The Role of Genetic Algorithm Selection Operators in Extending WSN Stability Period: A Comparative Study |
| title_sort | role of genetic algorithm selection operators in extending wsn stability period a comparative study |
| topic | genetic algorithm roulette wheel exponential selection rank selection tournament selection stochastic selection |
| url | https://www.mdpi.com/2079-9292/11/1/28 |
| work_keys_str_mv | AT ismaeljannoud theroleofgeneticalgorithmselectionoperatorsinextendingwsnstabilityperiodacomparativestudy AT yousefjaradat theroleofgeneticalgorithmselectionoperatorsinextendingwsnstabilityperiodacomparativestudy AT mohammadzmasoud theroleofgeneticalgorithmselectionoperatorsinextendingwsnstabilityperiodacomparativestudy AT ahmadmanasrah theroleofgeneticalgorithmselectionoperatorsinextendingwsnstabilityperiodacomparativestudy AT mohammadalia theroleofgeneticalgorithmselectionoperatorsinextendingwsnstabilityperiodacomparativestudy AT ismaeljannoud roleofgeneticalgorithmselectionoperatorsinextendingwsnstabilityperiodacomparativestudy AT yousefjaradat roleofgeneticalgorithmselectionoperatorsinextendingwsnstabilityperiodacomparativestudy AT mohammadzmasoud roleofgeneticalgorithmselectionoperatorsinextendingwsnstabilityperiodacomparativestudy AT ahmadmanasrah roleofgeneticalgorithmselectionoperatorsinextendingwsnstabilityperiodacomparativestudy AT mohammadalia roleofgeneticalgorithmselectionoperatorsinextendingwsnstabilityperiodacomparativestudy |