JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2014, Vol. 49 ›› Issue (08): 118-124.doi: 10.6040/j.issn.1671-9352.7.2014.002

Previous Articles    

A study of adaptive Memetic algorithm based on particle swarm optimization

QU Bin-peng1, WANG Zhi-hao2   

  1. 1. Department of Health Management, Shandong Medical College, Jinan 250002, Shandong, China;
    2. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, Shandong, China
  • Received:2014-07-01 Revised:2014-07-09 Published:2014-09-24

PDF (PC)

690

Abstract: According to the analyzing of the proposed adaptive Memetic algorithm,the Particle Swarm Optimization is used as Memetic algorithm's GS. An Adaptive Memetic Algorithm Based on Particle Swarm Optimization is constituted according to different types of Adaptive Memetic. The experimental results show that the PMemetic algorithm improves the global search capability, the convergence speed and the solution accuracy. Experimental results demonstrate that the algorithm can get the better optimal path.

Key words: local search, Memetic algorithm, particle swarm optimization

CLC Number: 

  • TP301
[1] KRASNOGOR N, SMITH J E. A tutorial for competent memetic algorithms: model, taxonomy and design issues[J]. IEEE Transactions on Evolutionary Computation, 2005, 9(5):474-488.
[2] MCKAY M, BECKMAN R, CONOVER W. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J]. Technometrics, 1979, 21(2):239-245.
[3] CRAWFORD B, CASTRO C, MONFROY E. A hyperheuristic approach for guiding enumeration in constraint solving[J]. Advances in Intelligent Systems and Computing, 2013, 175:171-188.
[4] MAASHI M, ZCAN E, KENDALL G. A multi-objective hyper-heuristic based on choice function[J]. Expert Systems with Applications, 2014, 41(9):4475-4493.
[5] KOULINAS G, ANAGNOSTOPOULOS K. A new tabu search-based hyper-heuristic algorithm for solving construction leveling problems with limited resource availabilities[J]. Automation in Construction, 2013, 31:169-175.
[6] DETTERER D, KWAN P. A co-evolving memetic wrapper for Herb-Herb interaction analysis in TCM informatics[J]. Lecture Notes in Computer Science, 2012, 7104:361-371.
[7] HUANG Wei-Hsiu, CHANG Pei-Chann, LIMB Meng-Hiot. Memes co-evolution strategies for fast convergence in solving single machine scheduling problems[J]. 2012, 50(24):7357-7377.
[8] NERI F, WEBER M, CARAFFINI F, et al. Meta-Lamarckian learning in three stage optimal memetic exploration[EB/OL]. Computational Intelligence (UKCI), 2012. http://www.researchgate.net/researcher/2004327828_M_Weber
[9] KANG S, YANG H, SCHOR L, et al. Multi-objective mapping optimization via problem decomposition for many-core systems[EB/OL]. 2012 IEEE 10th Symposium onEmbedded Systems for Real-time Multimedia (ESTIMedia), 2012. http://www.computer.org/csdl/proceedings/estimedia/2012/4968/00/06507026.pdf
[10] LIU Bo, WANG Ling, JIN Yi-Hui. An effective PSO-based memetic algorithm for flow shop scheduling[C]//IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2007, 37(1): 18-27. DOI: 10.1109/TSMCB.2006.883272
[1] MA Lan, LI Wei-an, YIN Tian-yi. Improved particle swarm optimization for flight conflict resolution based on variable neighborhood search [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(1): 23-28.
[2] WAN Zhi-ping1, L Zhi-min1,2 *. A kind of adaptive species optimization of wireless Mesh network QoS routing algorithm [J]. J4, 2013, 48(09): 10-16.
[3] L Jia-liang1,2,3, WANG Ying-long1,3, CUI Huan-qing1, WEI Nuo2,3, GUO Qiang2,3. Study on the node localization schemes of three dimensional wireless sensor networks based on particle swarm optimization [J]. J4, 2013, 48(05): 78-82.
[4] CUI Huan-qing1,2, WANG Ying-long1*, L Jia-liang1,2, WEI Nuo1. Distributed node localization based on the stochastic particle swarm optimization [J]. J4, 2012, 47(9): 51-55.
[5] ZHOU Yan1,2, LIU Pei-yu 1,2, ZHAO Jing1,2, WANG Qian-long1,2. Chaos particle swarm optimization based on the adaptive inertia weight [J]. J4, 2012, 47(3): 27-32.
[6] DING Wei-ping1,2,3, WANG Jian-dong2, DUAN Wei-hua2, SHI Quan1. Research of cooperative PSO for attribute reduction optimization [J]. J4, 2011, 46(5): 97-102.
[7] LI Bin1,2, LI Yi-bin1, RONG Xue-wen1. Intelligent optimization strategy for ELM-RBF neural networks [J]. J4, 2010, 45(5): 48-51.
[8] ZHOU Shu-wang1,2,3, WANG Ying-long1,3, GUO Qiang1,2, WEI Nuo1,2, GUO Wen-juan1,3. A new method for localization based on network  coverage and intelligent search [J]. J4, 2010, 45(11): 27-31.
[9] QI Lu1, YANG Bo1*, WANG Lin2. PSO based grayscale correction for cement paste image gathered by μCT [J]. J4, 2010, 45(11): 16-20.
[10] ZHOU Shu-Wang, WANG Yang-Long, GUO Jiang, WEI Nuo. Particle swarm optimizationbasedwireless sensor  network nodes localization method [J]. J4, 2009, 44(9): 52-55.
[11] TUN Rui-Hai, DONG Ji-Wen, DUAN Qi-Qiang. A scale chaos particle swarm optimization algorithm and the wavelet  in the forecast application of foundation settlement [J]. J4, 2009, 44(11): 75-78.
[12] ZHANG Guo-ying,SHA Yun,JIANG Hui-na . An improved KNN classification algorithm based on particle swarm optimization [J]. J4, 2006, 41(3): 34-36 .
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd