A new clonal selection immune algorithm with perturbation guiding search and non-uniform hypermutation

被引:0
|
作者
Zhao X. [1 ]
Liu G. [2 ]
Liu H. [2 ]
Zhao G. [2 ]
Niu S. [2 ]
机构
[1] Department of Mathematics, School of Science, Beijing University of Posts and Telecommunications, Beijing
[2] School of Computer Science, Beijing University of Posts and Telecommunications, Beijing
基金
中国国家自然科学基金;
关键词
Artificial immune system; Clonal selection; Non-uniform mutation; Particle swarm algorithm; Perturbation guiding search;
D O I
10.1080/18756891.2010.9727749
中图分类号
学科分类号
摘要
A new clonal selection immune algorithm with perturbation guiding search and non-uniform hypermutation (nCSIA) is proposed based on the idea of perturbed particle swarm algorithm and non-uniform mutation. The proposed algorithm proportional clones antibody based on the affinity, adaptively adjusts the searching steps of antibodies with hypermutation according to the adaptive variation rule of non-uniform mutation and chooses the promising antibody based on the affinity by clonal selection principle. In order to keep the balance of exploration/exploitation better, perturbation guiding search strategy is presented, which is actually an elitist learning mechanism and is borrowed from the perturbed particle swarm algorithm. In order to validate the effectiveness of nCSIA, comprehensive experiments and analysis are done based on fifteen unimodal or multimodal benchmark functions. Compared with standard and the recent algorithms, it indicates that the proposed algorithm is feasible, effective and has better performance in terms of convergence, accuracy and stability. More evident predominance emerges from further experimental comparisons with expanding search space and increasing dimensions. © 2010 Taylor & Francis Group, LLC.
引用
收藏
页码:1 / 17
页数:16
相关论文
共 50 条
  • [41] An algorithm to determine the chromaticity under non-uniform illuminant
    Ratnasingam, Sivalogeswaran
    Collins, Steve
    IMAGE AND SIGNAL PROCESSING, 2008, 5099 : 244 - 253
  • [42] A Trajectory Compression Algorithm Based on Non-uniform Quantization
    Lv, Chengjiao
    Chen, Feng
    Xu, Yongzhi
    Song, Junping
    Lv, Pin
    2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), 2015, : 2469 - 2474
  • [43] CLASSIFICATION ALGORITHM FOR THE NON-UNIFORM SAMPLING OF SIGNALS AND NOISE
    PROKOPENKO, IG
    IZVESTIYA VYSSHIKH UCHEBNYKH ZAVEDENII RADIOELEKTRONIKA, 1989, 32 (09): : 8 - 12
  • [44] Convergent Analysis on Evolutionary Algorithm with Non-uniform Mutation
    Zhao, Xinchao
    2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8, 2008, : 940 - 944
  • [45] Designing Non-Uniform Constellations using Genetic Algorithm
    Jerji, Fadi
    Akamine, Cristiano
    Omar, Nizam
    2020 IEEE INTERNATIONAL SYMPOSIUM ON BROADBAND MULTIMEDIA SYSTEMS AND BROADCASTING (BMSB), 2020,
  • [46] A non-uniform NSAF-SF adaptive algorithm
    Xavier, P. P. S.
    Petraglia, M. R.
    Haddad, D. B.
    ELECTRONICS LETTERS, 2021, 57 (01) : 38 - 41
  • [47] A non-uniform image compression using Genetic Algorithm
    Bonyadi, M. R.
    Dehghani, E.
    Moghaddam, Mohsen Ebrahimi
    PROCEEDINGS OF IWSSIP 2008: 15TH INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING, 2008, : 315 - 318
  • [48] Secure Similarity Search Over Encrypted Non-Uniform Datasets
    Guo, Cheng
    Liu, Wanping
    Liu, Ximeng
    Zhang, Yinghui
    IEEE TRANSACTIONS ON CLOUD COMPUTING, 2022, 10 (03) : 2102 - 2117
  • [49] Reinforcement Learning with Non-uniform State Representations for Adaptive Search
    Manjanna, Sandeep
    van Hoof, Herke
    Dudek, Gregory
    2018 IEEE INTERNATIONAL SYMPOSIUM ON SAFETY, SECURITY, AND RESCUE ROBOTICS (SSRR), 2018,
  • [50] An efficient hybrid search algorithm for robust and accurate image alignment under non-uniform illumination variations
    Lai, SH
    Fang, M
    MACHINE VISION APPLICATIONS IN INDUSTRIAL INSPECTION IX, 2001, 4301 : 154 - 165