A comparison of computational efforts between particle swarm optimization and genetic algorithm for identification of fuzzy models

被引:23
作者
Khosla, Arun [1 ]
Kumar, Shakti [2 ]
Ghosh, Kumar Rahul [3 ]
机构
[1] Natl Inst Technol, Dept Elect & Commun Engn, Jalandhar 144011, India
[2] Soc Educ & Res, Jagadhari 135003, India
[3] Aricent Inc, Gurgaon 122015, India
来源
NAFIPS 2007 - 2007 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY | 2007年
关键词
D O I
10.1109/NAFIPS.2007.383845
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Fuzzy systems are rule-based systems that provide a framework for representing and processing information in a way that resembles human communication and reasoning process. Fuzzy modeling or fuzzy model identification is an arduous task, demanding the identification of many parameters that can be viewed as an optimization process. Evolutionary algorithms are well suited to the problem of fuzzy modeling because they are able to search complex and high dimensional search space while being able to avoid local minima (or maxima). The Particle Swarm Optimization (PSO) algorithm, like other evolutionary algorithms, is a stochastic technique based on the metaphor of social interaction. PSO is similar to the Genetic Algorithm (GA) as these two evolutionary heuristics are population-based search methods. The main objective of this paper is to present the tremendous savings in computational efforts that can be achieved through the use of PSO algorithm in comparison to GA, when used for the identification of fuzzy models from the available input-output data. For realistic comparison, the training data, models complexity and some other common parameters that influence the computational efforts considerably are not changed. The real data from the rapid Nickel-Cadmium (Ni-Cd) battery charger developed has been used for the purpose of illustration and simulation purposes.
引用
收藏
页码:245 / +
页数:2
相关论文
共 50 条
  • [41] Identification of visco-elastic models for rocks using genetic programming coupled with the modified particle swarm optimization algorithm
    Feng, Xia-Ting
    Chen, Bing-Rui
    Yang, Chengxiang
    Zhou, Hui
    Ding, Xiuli
    INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES, 2006, 43 (05) : 789 - 801
  • [42] Constrained optimization by the ε constrained hybrid algorithm of particle swarm optimization and genetic algorithm
    Takahama, T
    Sakai, S
    Iwane, N
    AI 2005: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2005, 3809 : 389 - 400
  • [43] Comparison between ant colony algorithm and particle swarm optimization and their application in VRP
    Shi Kai
    Cai Yan-Guang
    Zou Gu-Shan
    Wang Tao
    PROCEEDINGS OF THE 24TH CHINESE CONTROL CONFERENCE, VOLS 1 AND 2, 2005, : 1596 - 1599
  • [44] Study comparison between firefly algorithm and particle swarm optimization for SLAM problems
    Janah, Mounia
    Fujimoto, Yasutaka
    2018 INTERNATIONAL POWER ELECTRONICS CONFERENCE (IPEC-NIIGATA 2018 -ECCE ASIA), 2018, : 681 - 687
  • [45] Cash balance management: A comparison between genetic algorithms and particle swarm optimization
    da Costa Moraes, Marcelo Botelho
    Nagano, Marcelo Seido
    ACTA SCIENTIARUM-TECHNOLOGY, 2012, 34 (04) : 373 - 379
  • [46] Comparison between Differential Evolution Algorithm and Particle Swarm Optimization for Market Clearing with Voltage Dependent Load Models
    Kiran, Deep
    Panigrahi, Bijaya Ketan
    Abhyankar, A. R.
    SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, PT I (SEMCCO 2013), 2013, 8297 : 209 - 221
  • [47] Two Novel Particle Swarm Optimization Algorithm Models
    Song, Shengli
    Kong, Li
    Cheng, Jingjing
    2009 INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS, VOL 2, PROCEEDINGS, 2009, : 440 - +
  • [48] A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization
    Sun, Tao
    Xu, Ming-hai
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2017, 2017
  • [49] Comparison of Particle Swarm Optimization and Genetic Algorithm for the Path Loss Reduction in an Urban Area
    Chiu, Chien-Ching
    Cheng, Yu-Ting
    Chang, Chai-Wei
    JOURNAL OF APPLIED SCIENCE AND ENGINEERING, 2012, 15 (04): : 371 - 380
  • [50] Comparison of particle swarm optimization and genetic algorithm for the path loss reduction in an urban area
    Chiu, Chien-Ching
    Cheng, Yu-Ting
    Chang, Chai-Wei
    Journal of Applied Science and Engineering, 2012, 15 (04): : 371 - 380