Enforced Mutation to Enhancing the Capability of Particle Swarm Optimization Algorithms

被引:0
|
作者
Chou, PenChen [1 ]
Chen, JenLian [1 ]
机构
[1] DaYeh Univ, Dept Elect Engn, Changhua 41000, Chunghwa County, Taiwan
来源
ADVANCES IN SWARM INTELLIGENCE, PT I | 2011年 / 6728卷
关键词
Optimization; Optimization Benchmark; Particle Swarm Optimization; Genetic Algorithm; Mutation;
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), proposed by Professor Kennedy and Eberhart in 1995, attracts many attentions to solve for a lot of optimization problems nowadays. Due to its simplicity of setting-parameters and computational efficiency, it becomes one of the most popular algorithms in optimizations. However, the discrepancy of PSO is the low dimensionality of the problem can be solved. Once the optimized function becomes complicated, the efficiency gained in PSO degradates rapidly. More complex algorithms on PSO required. Therefore, different algorithms will be applied to different problems with difficulties. Three different algorithms are suggested to solve different problems accordinately. In summary, proposed PSO algorithms apply well to problems with different difficulties in the final simulations.
引用
收藏
页码:28 / 37
页数:10
相关论文
共 50 条
  • [1] Modified particle swarm optimization algorithms based on topology and particle mutation
    Xu S.-C.
    Cai J.
    Cheng Y.
    Wang H.-X.
    Kongzhi yu Juece/Control and Decision, 2019, 34 (02): : 419 - 428
  • [2] ON ENHANCING EFFICIENCY AND ACCURACY OF PARTICLE SWARM OPTIMIZATION ALGORITHMS
    Chiaradonna, Silvano
    Di Giandomenico, Felicita
    Murru, Nadir
    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2015, 11 (04): : 1165 - 1189
  • [3] Particle Swarm Optimization with Controlled Mutation
    Higashitani, Mitusharu
    Ishigame, Atsushi
    Yasuda, Keiichiro
    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2007, 2 (02) : 192 - 194
  • [4] Improved particle swarm optimization algorithms for electromagnetic optimization
    Mussetta, Marco
    Selleri, Stefano
    Pirinoli, Paola
    Zich, Riccardo E.
    Matekovits, Ladislau
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2008, 19 (01) : 75 - 84
  • [5] A MODIFIED PARTICLE SWARM OPTIMIZATION WITH MUTATION AND REPOSITION
    Ratanavilisacul, Chiabwoot
    Kruatrachue, Boontee
    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2014, 10 (06): : 2127 - 2142
  • [6] Particle Swarm Optimization with Adaptive Mutation Operator
    Chen, Yujuan
    DCABES 2008 PROCEEDINGS, VOLS I AND II, 2008, : 710 - 713
  • [7] Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms
    Kalyanmoy Deb
    Nikhil Padhye
    Computational Optimization and Applications, 2014, 57 : 761 - 794
  • [8] Optimization of Greenhouse Climate Model Parameters Using Particle Swarm Optimization and Genetic Algorithms
    Hasni, Abdelhafid
    Taibi, Rachid
    Draoui, Belkacem
    Boulard, Thierry
    IMPACT OF INTEGRATED CLEAN ENERGY ON THE FUTURE OF THE MEDITERRANEAN ENVIRONMENT, 2011, 6 : 371 - 380
  • [9] Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms
    Deb, Kalyanmoy
    Padhye, Nikhil
    COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 2014, 57 (03) : 761 - 794
  • [10] Adaptive particle swarm optimization algorithms
    Ai, The Jin
    Kachitvichyanukul, Voratas
    PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INTELLIGENT LOGISTICS SYSTEMS, 2008, : 460 - 469