A Memetic Particle Swarm Optimization Algorithm To Solve Multi-objective Optimization Problems

被引：3
作者：
Li Xin [1 ]
Wei Jingxuan [1 ]
Liu Yang [1 ]
机构：
[1] Xidian Univ, Sch Comp Sci & Technol, Xian, Shaanxi, Peoples R China
来源：
2017 13TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS) | 2017年
基金：
美国国家科学基金会;
关键词：
component; Memetic Algorithm; Mutation; Particle Swarm Optimization; Simulated Annealing;
D O I：
10.1109/CIS.2017.00018
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An improved particle swarm optimization algorithm to solve multi-objective optimization problems is proposed, called MoMPSO. Firstly, Simulated annealing is incorporated to Particle swarm optimization to enhance the search ability. Secondly, Nonuniform mutation is used to increase the diversity. The simulated results show that the proposed algorithm is better than the compared ones.
引用
收藏
页码：44 / 48
页数：5
相关论文
共 13 条
  • [1] Agarwal A, 2017, IEEE INT C COMP INT, P1
  • [2] Andrews P S., 2006, EVOLUTIONARY COMPUTA, P1044
  • [3] A Memetic Algorithm for the Traveling Salesman Problem
    Arango, M. D.
    Serna, C. A.
    [J]. IEEE LATIN AMERICA TRANSACTIONS, 2015, 13 (08) : 2674 - 2679
  • [4] Askarzadeh A, 2017, IEEE INT C SYST MAN
  • [5] Coello C A C, 2004, HANDLING MULTIPLE OB, P256
  • [6] Coello C.A. Coello., 2002, MOPSO PROPOSAL MULTI
  • [7] Deb K, 2001, LECT NOTES COMPUT SC, V1993, P284
  • [8] A fast and elitist multiobjective genetic algorithm: NSGA-II
    Deb, K
    Pratap, A
    Agarwal, S
    Meyarivan, T
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) : 182 - 197
  • [9] Gong Mao-Guo, 2009, Journal of Software, V20, P271, DOI 10.3724/SP.J.1001.2009.03483
  • [10] Ishibuchi H, 2017, IEEE INT C SYST MAN
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