Studying the Impact of Initialization for Population-Based Algorithms with Low-Discrepancy Sequences

• Main Authors: Adnan Ashraf, Sobia Pervaiz, Waqas Haider Bangyal, Kashif Nisar, Ag. Asri Ag. Ibrahim, Joel J. P. C. Rodrigues, Danda B. Rawat
• Format: Article
• Language: English; English
• Published: MDPI AG, Basel, Switzerland 2021
• Subjects: QA273-280 Probabilities. Mathematical statistics; QA75.5-76.95 Electronic computers. Computer science
• 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; https://eprints.ums.edu.my/id/eprint/31833/; https://www.mdpi.com/2076-3417/11/17/8190; https://www.mdpi.com/journal/applsci

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...