Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences
To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. These algorithms can affect convergence to identify a robust optimum solution. To...
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| Format: | Article |
| Language: | English English |
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MDPI AG, Basel, Switzerland
2021
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| Online Access: | https://eprints.ums.edu.my/id/eprint/31833/1/Studying%20the%20Impact%20of%20Initialization%20for%20Population-Based%20Algorithms%20with%20Low-Discrepancy%20Sequences.pdf https://eprints.ums.edu.my/id/eprint/31833/2/Studying%20the%20Impact%20of%20Initialization%20for%20Population-Based%20Algorithms%20with%20Low-Discrepancy%20Sequences1.pdf |
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| author | Adnan Ashraf Sobia Pervaiz Waqas Haider Bangyal Kashif Nisar Ag. Asri Ag. Ibrahim Joel J. P. C. Rodrigues Danda B. Rawat |
| author_facet | Adnan Ashraf Sobia Pervaiz Waqas Haider Bangyal Kashif Nisar Ag. Asri Ag. Ibrahim Joel J. P. C. Rodrigues Danda B. Rawat |
| author_sort | Adnan Ashraf |
| collection | UMS |
| description | To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. These algorithms can affect convergence to identify a robust optimum solution. To investigate the effectiveness of diversity, many scholars have a focus on the reliability and quality of meta-heuristic algorithms for enhancement. To initialize the population in the search space, this dissertation proposes three new low discrepancy sequences for population initialization instead of uniform distribution called the WELL sequence, Knuth sequence, and Torus sequence. This paper also introduces a detailed survey of the different initialization methods of PSO and DE based on quasi-random sequence families such as the Sobol sequence, Halton sequence, and uniform random distribution. For well-known benchmark test problems and learning of artificial neural network, the proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO), BA (BA-TO, BA-WE, and BA-KN), and DE (DE-TO, DE-WE, and DE-KN) have been evaluated. The synthesis of our strategies demonstrates promising success over uniform random numbers using low discrepancy sequences. The experimental findings indicate that the initialization based on low discrepancy sequences is exceptionally stronger than the uniform random number. Furthermore, our work outlines the profound effects on convergence and heterogeneity of the proposed methodology. It is expected that a comparative simulation survey of the low discrepancy sequence would be beneficial for the investigator to analyze the meta-heuristic algorithms in detail. |
| first_indexed | 2024-03-06T03:13:57Z |
| format | Article |
| id | ums.eprints-31833 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:13:57Z |
| publishDate | 2021 |
| publisher | MDPI AG, Basel, Switzerland |
| record_format | dspace |
| spelling | ums.eprints-318332022-03-04T07:53:50Z https://eprints.ums.edu.my/id/eprint/31833/ Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences Adnan Ashraf Sobia Pervaiz Waqas Haider Bangyal Kashif Nisar Ag. Asri Ag. Ibrahim Joel J. P. C. Rodrigues Danda B. Rawat QA273-280 Probabilities. Mathematical statistics QA75.5-76.95 Electronic computers. Computer science To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. These algorithms can affect convergence to identify a robust optimum solution. To investigate the effectiveness of diversity, many scholars have a focus on the reliability and quality of meta-heuristic algorithms for enhancement. To initialize the population in the search space, this dissertation proposes three new low discrepancy sequences for population initialization instead of uniform distribution called the WELL sequence, Knuth sequence, and Torus sequence. This paper also introduces a detailed survey of the different initialization methods of PSO and DE based on quasi-random sequence families such as the Sobol sequence, Halton sequence, and uniform random distribution. For well-known benchmark test problems and learning of artificial neural network, the proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO), BA (BA-TO, BA-WE, and BA-KN), and DE (DE-TO, DE-WE, and DE-KN) have been evaluated. The synthesis of our strategies demonstrates promising success over uniform random numbers using low discrepancy sequences. The experimental findings indicate that the initialization based on low discrepancy sequences is exceptionally stronger than the uniform random number. Furthermore, our work outlines the profound effects on convergence and heterogeneity of the proposed methodology. It is expected that a comparative simulation survey of the low discrepancy sequence would be beneficial for the investigator to analyze the meta-heuristic algorithms in detail. MDPI AG, Basel, Switzerland 2021 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/31833/1/Studying%20the%20Impact%20of%20Initialization%20for%20Population-Based%20Algorithms%20with%20Low-Discrepancy%20Sequences.pdf text en https://eprints.ums.edu.my/id/eprint/31833/2/Studying%20the%20Impact%20of%20Initialization%20for%20Population-Based%20Algorithms%20with%20Low-Discrepancy%20Sequences1.pdf Adnan Ashraf and Sobia Pervaiz and Waqas Haider Bangyal and Kashif Nisar and Ag. Asri Ag. Ibrahim and Joel J. P. C. Rodrigues and Danda B. Rawat (2021) Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences. Applied Sciences, 11. pp. 1-41. ISSN 2076-3417 https://www.mdpi.com/2076-3417/11/17/8190 https://www.mdpi.com/journal/applsci https://www.mdpi.com/journal/applsci |
| spellingShingle | QA273-280 Probabilities. Mathematical statistics QA75.5-76.95 Electronic computers. Computer science Adnan Ashraf Sobia Pervaiz Waqas Haider Bangyal Kashif Nisar Ag. Asri Ag. Ibrahim Joel J. P. C. Rodrigues Danda B. Rawat Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences |
| title | Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences |
| title_full | Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences |
| title_fullStr | Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences |
| title_full_unstemmed | Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences |
| title_short | Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences |
| title_sort | studying the impact of initialization for population based algorithms with low discrepancy sequences |
| topic | QA273-280 Probabilities. Mathematical statistics QA75.5-76.95 Electronic computers. Computer science |
| url | https://eprints.ums.edu.my/id/eprint/31833/1/Studying%20the%20Impact%20of%20Initialization%20for%20Population-Based%20Algorithms%20with%20Low-Discrepancy%20Sequences.pdf https://eprints.ums.edu.my/id/eprint/31833/2/Studying%20the%20Impact%20of%20Initialization%20for%20Population-Based%20Algorithms%20with%20Low-Discrepancy%20Sequences1.pdf |
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