Hybrid particle swarm - Evolutionary algorithm for search and optimization

被引:0
|
作者
Grosan, C [1 ]
Abraham, A
Han, SY
Gelbukh, A
机构
[1] Univ Babes Bolyai, Dept Comp Sci, R-3400 Cluj Napoca, Romania
[2] Chung Ang Univ, Sch Comp Sci & Engn, Seoul 156756, South Korea
[3] IPN, CIC, Mexico City 07738, DF, Mexico
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO - evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations the geometrical place consists more than one single point. The performance of the newly proposed PSO algorithm is compared with evolutionary algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm, combining PSO and evolutionary algorithms. The hybrid combination is able to detect the geometrical place very fast for which the evolutionary algorithms required more time and the conventional PSO approach even failed to find the real geometrical place.
引用
收藏
页码:623 / 632
页数:10
相关论文
共 50 条
  • [1] A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm
    Dziwinski, Piotr
    Bartczuk, Lukasz
    Goetzen, Piotr
    [J]. ARTIFICIAL INTELLIGENCEAND SOFT COMPUTING, PT I, 2019, 11508 : 432 - 444
  • [2] Hybrid particle swarm optimization and pattern search algorithm
    Koessler, Eric
    Almomani, Ahmad
    [J]. OPTIMIZATION AND ENGINEERING, 2021, 22 (03) : 1539 - 1555
  • [3] Hybrid particle swarm optimization and pattern search algorithm
    Eric Koessler
    Ahmad Almomani
    [J]. Optimization and Engineering, 2021, 22 : 1539 - 1555
  • [4] Particle swarm optimization with genetic recombination: a hybrid evolutionary algorithm
    Duong, Sam Chau
    Kinjo, Hiroshi
    Uezato, Eiho
    Yamamoto, Tetsuhiko
    [J]. ARTIFICIAL LIFE AND ROBOTICS, 2010, 15 (04) : 444 - 449
  • [5] A novel hybrid gravitational search particle swarm optimization algorithm
    Khan, Talha Ali
    Ling, Sai Ho
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2021, 102
  • [6] A hybrid search strategy based particle swarm optimization algorithm
    Wang, Qian
    Wang, Pei-hong
    Su, Zhi-gang
    [J]. PROCEEDINGS OF THE 2013 IEEE 8TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2013, : 301 - 306
  • [7] Binary optimization using hybrid particle swarm optimization and gravitational search algorithm
    Seyedali Mirjalili
    Gai-Ge Wang
    Leandro dos S. Coelho
    [J]. Neural Computing and Applications, 2014, 25 : 1423 - 1435
  • [8] A Hybrid Optimization Method of Beetle Antennae Search Algorithm and Particle Swarm Optimization
    Lin, Mei-jin
    Li, Qing-hao
    [J]. 2018 INTERNATIONAL CONFERENCE ON ELECTRICAL, CONTROL, AUTOMATION AND ROBOTICS (ECAR 2018), 2018, 307 : 396 - 401
  • [9] Local search based hybrid particle swarm optimization algorithm for multiobjective optimization
    Mousa, A. A.
    El-Shorbagy, M. A.
    Abd-El-Wahed, W. F.
    [J]. SWARM AND EVOLUTIONARY COMPUTATION, 2012, 3 : 1 - 14
  • [10] Binary optimization using hybrid particle swarm optimization and gravitational search algorithm
    Mirjalili, Seyedali
    Wang, Gai-Ge
    Coelho, Leandro dos S.
    [J]. NEURAL COMPUTING & APPLICATIONS, 2014, 25 (06): : 1423 - 1435