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

被引:18
|
作者
Ashraf, Adnan [1 ]
Pervaiz, Sobia [2 ]
Bangyal, Waqas Haider [3 ]
Nisar, Kashif [4 ]
Ibrahim, Ag Asri Ag [4 ]
Rodrigues, Joel J. P. C. [5 ,6 ]
Rawat, Danda B. [7 ]
机构
[1] GC Women Univ Sialkot, IT Support Ctr, Punjab 51310, Pakistan
[2] Abasyn Univ, Dept Comp Sci, Islamabad 45710, Pakistan
[3] Univ Malaysia Sabah, Fac Comp & Informat, Jalan UMS, Kota Kinabalu 88400, Sabah, Malaysia
[4] Univ Gujrat, Dept Comp Sci, Punjab 50700, Pakistan
[5] Fed Univ Piaui UFPI, Ctr Tecnol, Campus Petronio Portela, BR-64049550 Teresina, Brazil
[6] Inst Telecomunicacoes, P-6201001 Covilha, Portugal
[7] Howard Univ, Data Sci & Cybersecur Ctr, Dept Elect Engn & Comp Sci, Washington, DC 20059 USA
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 17期
关键词
Knuth sequence; premature convergence; quasi-random sequences; Torus sequence; training of artificial neural network; WELL sequence; PARTICLE SWARM OPTIMIZATION; DIFFERENTIAL EVOLUTION ALGORITHM; BAT ALGORITHM; PSO;
D O I
10.3390/app11178190
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页数:41
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