A Normal Distributed Dwarf Mongoose Optimization Algorithm for Global Optimization and Data Clustering Applications

被引:31
|
作者
Aldosari, Fahd [1 ]
Abualigah, Laith [2 ]
Almotairi, Khaled H. [3 ]
机构
[1] Umm Al Qura Univ, Comp & Informat Syst Coll, Mecca 21955, Saudi Arabia
[2] Amman Arab Univ, Fac Comp Sci & Informat, Amman 11953, Jordan
[3] Umm Al Qura Univ, Comp Engn Dept, Mecca 21955, Saudi Arabia
来源
SYMMETRY-BASEL | 2022年 / 14卷 / 05期
关键词
dwarf mongoose optimization algorithm (DMOA); generalized normal distribution; opposition-based learning; optimization algorithm; benchmark functions; data clustering problems; SEARCH ALGORITHM;
D O I
10.3390/sym14051021
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
As data volumes have increased and difficulty in tackling vast and complicated problems has emerged, the need for innovative and intelligent solutions to handle these difficulties has become essential. Data clustering is a data mining approach that clusters a huge amount of data into a number of clusters; in other words, it finds symmetric and asymmetric objects. In this study, we developed a novel strategy that uses intelligent optimization algorithms to tackle a group of issues requiring sophisticated methods to solve. Three primary components are employed in the suggested technique, named GNDDMOA: Dwarf Mongoose Optimization Algorithm (DMOA), Generalized Normal Distribution (GNF), and Opposition-based Learning Strategy (OBL). These parts are used to organize the executions of the proposed method during the optimization process based on a unique transition mechanism to address the critical limitations of the original methods. Twenty-three test functions and eight data clustering tasks were utilized to evaluate the performance of the suggested method. The suggested method's findings were compared to other well-known approaches. In all of the benchmark functions examined, the suggested GNDDMOA approach produced the best results. It performed very well in data clustering applications showing promising performance.
引用
收藏
页数:28
相关论文
共 50 条
  • [41] A New Integral Function Algorithm for Global Optimization and Its Application to the Data Clustering Problem
    Pandiya R.
    Ahdika A.
    Khomsah S.
    Ramadhani R.D.
    Mendel, 2023, 29 (02) : 162 - 168
  • [42] A New Parallel Tempering Algorithm for Global Optimization: Applications to Bioprocess Optimization
    Ochoa, Silvia
    Repke, Jens-Uwe
    Wozny, Guenter
    19TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, 2009, 26 : 513 - 518
  • [43] An improved polar lights optimization algorithm for global optimization and engineering applications
    Tianping Huang
    Fagou Huang
    Zhaohui Qin
    Jiafang Pan
    Scientific Reports, 15 (1)
  • [44] A Novel Improved Whale Optimization Algorithm for Global Optimization and Engineering Applications
    Liang, Ziying
    Shu, Ting
    Ding, Zuohua
    MATHEMATICS, 2024, 12 (05)
  • [45] Distributed Stochastic Algorithm for Global Optimization in Networked System
    Wang, Shengnan
    Li, Chunguang
    JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2018, 179 (03) : 1001 - 1007
  • [46] A DISTRIBUTED SEARCH ALGORITHM FOR GLOBAL OPTIMIZATION ON NUMERICAL SPACES
    COURRIEU, P
    RAIRO-RECHERCHE OPERATIONNELLE-OPERATIONS RESEARCH, 1993, 27 (03): : 281 - 292
  • [47] Query Optimization using Clustering and Genetic Algorithm for Distributed Databases
    Lakshmi, S. Venkata
    Vatsavayi, Valli Kumari
    2016 INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND INFORMATICS (ICCCI), 2016,
  • [48] Distributed Stochastic Algorithm for Global Optimization in Networked System
    Shengnan Wang
    Chunguang Li
    Journal of Optimization Theory and Applications, 2018, 179 : 1001 - 1007
  • [49] Solving Packing Problems by a Distributed Global Optimization Algorithm
    Hu, Nian-Ze
    Li, Han-Lin
    Tsai, Jung-Fa
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2012, 2012
  • [50] Social group optimization for global optimization of multimodal functions and data clustering problems
    Anima Naik
    Suresh Chandra Satapathy
    Amira S. Ashour
    Nilanjan Dey
    Neural Computing and Applications, 2018, 30 : 271 - 287