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 PopulationBased Algorithms with LowDiscrepancy Sequences. Applied Sciences, 11. pp. 141. ISSN 20763417
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Abstract
To solve different kinds of optimization challenges, metaheuristic algorithms have been extensively used. Population initialization plays a prominent role in metaheuristic 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 metaheuristic 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 quasirandom sequence families such as the Sobol sequence, Halton sequence, and uniform random distribution. For wellknown benchmark test problems and learning of artificial neural network, the proposed methods for PSO (TOPSO, KNPSO, and WEPSO), BA (BATO, BAWE, and BAKN), and DE (DETO, DEWE, and DEKN) 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 metaheuristic algorithms in detail.
Item Type:  Article 

Keyword:  Knuth sequence , Premature convergence , Quasirandom sequences , Torus sequence , Training of artificial neural network , WELL sequence 
Subjects:  Q Science > QA Mathematics > QA1939 Mathematics > QA273280 Probabilities. Mathematical statistics Q Science > QA Mathematics > QA1939 Mathematics > QA7190 Instruments and machines > QA75.576.95 Electronic computers. Computer science 
Department:  FACULTY > Faculty of Computing and Informatics 
Depositing User:  SITI AZIZAH BINTI IDRIS  
Date Deposited:  04 Mar 2022 15:53 
Last Modified:  04 Mar 2022 15:53 
URI:  https://eprints.ums.edu.my/id/eprint/31833 
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