Computer Science ›› 2020, Vol. 47 ›› Issue (2): 186-194.doi: 10.11896/jsjkx.181202338

• Artificial Intelligence • Previous Articles     Next Articles

Protected Zone-based Population Migration Dynamics Optimization Algorithm

HUANG Guang-qiu,LU Qiu-qin   

  1. (School of Management,Xi’an University of Architecture and Technology,Xi’an 710055,China)
  • Received:2018-12-17 Online:2020-02-15 Published:2020-03-18
  • About author:HUANG Guang-qiu,born in 1964,Ph.D,professor,Ph.D supervisor.His main research interests include Petri-net theo-ry and application,system dynamics,swarm intelligent optimization algorithm and computer simulation;LU Qiu-qin,born in 1966,Ph.D,professor.Her main research interests include Petri-net theory and application,swarm intelligent optimization algorithm and numerical simulation.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (71874134), Project of Social Science Foundation of Shaanxi Province (2018S49, 2017S035), Key Project of Natural Science Basic Research Plan of Shaanxi Province (2019JZ-30), Humanity and Social Science Programming Foundation of Ministry of Education of China (15YJA910002) and Key Research Base Project of Philosophy and Social Sciences of Shaanxi Provincial Department of Education (18JZ036).

Abstract: To solve global optimum solutions of some complex optimization problems,a new swarm intelligence optimization algorithm,called PZPMDO,was proposed.In this algorithm,it is assumed that many biological populations live in an ecosystem,and the ecosystem is divided into two regions:non-protected zone and protected zone.All kinds of protection should be carried out for biological populations in the protected zone.There is a population migration channel between the non-protected zone and the protected zone.If the density of a biological population in a certain region is too high,the population will migrate to the low density region spontaneously,resulting in the influence on biological populations in the low density zone by the migrated biological population.The greater the proportion of a biological population,the greater the influence of the population.The stronger a biological population is,the more the biological population will spread its advantages to other biological populations.There is a mutual influe-nce on the survival and competition of each population in different zones,which is reflected in the interaction among the features of biological populations,and the influence varies with time.The ZGI index is used to describe the strength of a biological population.The protected zone-based population migration dynamic model,population migration and interaction of biological populations are used to construct operators.PZPMDO has 8 operators,and only 1/1000~1/100 of of total variables are dealt with at a time of evolution.The algorithm has the characteristics of fast search speed and global convergence,it is suitable for solving the global optimization problem with higher dimensions.

Key words: Global convergence, Population dynamic optimization algorithm, Protected zone-based population migration dynamics, Swarm intelligence optimization algorithm

CLC Number: 

  • TP301.6
[1]IZTOK F,ANDRES I,AKEMI G.Novelty search for global optimization [J].Applied Mathematics and Computation,2019,347(4):865-881.
[2]WOLPERT D H,MACREADY W G.No Free Lunch Theorems for Optimization[J].IEEE Transactions on Evolutionary Computation,1997,1(1):67-82.
[3]LUAN J,YAO Z,ZHAO F T.novel method to solve supplier selection problem:Hybrid algorithm of genetic algorithm and ant colony optimization[J].Mathematics and Computers in Si-mulation,2019,156:294-309.
[4]ZHANG Q,XIONG S W.Routing optimization of emergency grain distribution vehicles using the immune ant colony optimization algorithm[J].Applied Soft Computing,2018,71(6):917-925.
[5]BISWAJIT J,SUMAN M,SRIYANKAR A.Repository and Mutation based Particle Swarm Optimization (RMPSO):A new PSO variant applied to reconstruction of Gene Regulatory Network [J].Applied Soft Computing,2019,74:330-355.
[6]ZHU X H,NI Z W,CHENG M Y.Selective ensemble based on extreme learning machine and improved discrete artificial fish swarm algorithm for haze forecast [J].Applied Intelligence,2018,48(7): 1757-1775.
[7]GUO J H,LI H C,YANG H D.A collaborative detection approach for internal steam leakage of tyre vulcanization workshop with artificial immune algorithm[J].Computational & Applied Mathematics,2018,37(4):4219-4236.
[8]HUANG Q J,ZHANG K,SONG J C.Adaptive differential evolution with a Lagrange interpolation argument algorithm[J].Information Sciences,2019,472:180-202.
[9]HUANG G Q,LIU Q C,LU Q Q.Metapopulation Biogeography-Inspired Optimization[J].Journal of System Simulation,2014,26(6):1217-1224.
[10]MERNIK M,LIU S H,KARABOGA D,et al.On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation[J].Information Sciences,2015,291:115-127.
[11]HUANG G Q.SIS epidemic model-based optimization[J].Journal of Computational Science,2014,5:32-50.
[12]HUANG G Q.Function optimization algorithm based on SIRQV epidemic dynamic model[J].Journal of Computation Science,2015,8:62-92.
[13]HUANG G.Artificial memory optimization[J].Applied Soft Computing,2017,61:497-526.
[14]陈兰荪,孟新柱,焦建军.生物动力学[M].北京:科学出版社,2009:77-155.
[15]IISUFESCU M.Finite Markov Processes and Their Applications[M].Wiley:Chichester,1980.
[16]LIANG J J,QU B Y,SUGANTHAN P N,et al.Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization[R/OL].Nanyang Technological University,Tech.Rep.,2013.http://www.ntu.edu.sg/home/epnsugan/ index_files/ cec-2013/ Definitions of CEC 13 benchmark suite 0117.pdf.
[17] CHUANG Y C,CHEN C T,HWANG C.A simple and efficient real-coded genetic algorithm for constrained optimization[J].Applied Soft Computing,2016,38:87-105.
[18]KOROŠEC P,ŠILC J, FILIPIC B.The differential ant-stigmergy algorithm[J].Information Sciences,2012,192:82-97.
[19]BEHESHTI Z,SHAMSUDDIN S M.Non-parametric particle swarm optimization for global optimization[J].Applied Soft Computing,2015,28:345-359.
[20]AL-ROOMI A R,EL-HAWARY M E.Metropolis biogeography-based optimization[J].Information Sciences,2016,360:73-95.
[21]MUKHERJEE R,DEBCHOUDHURY S,DAS S.Modified differential evolution with locality induced genetic operators for dynamic optimization[J].European Journal of Operational Research,2016,253:337-355.
[22]ZHAO Z W,YANG J M,HU Z Y,et al.A differential evolution algorithm with self-adaptive strategy and control parameters based on symmetric Latin hypercube design for unconstrained optimization problems[J].European Journal of Operational Research,2016,250(1):30-45.
[23] CREPINŠEK M,LIU S H,MERNIK M.Replication and comparison of computational experiments in applied evolutionary computing:Common pitfalls and guidelines to avoid them[J].Applied Soft Computing,2014,19:161-170.
[24]DERRAC J,GARCÍA S,MOLINA D,et al.A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J].Swarm and Evolutionary Computation,2011,1:3-18.
[1] ZHANG Xin-ming, LI Shuang-qian, LIU Yan, MAO Wen-tao, LIU Shang-wang, LIU Guo-qi. Coyote Optimization Algorithm Based on Information Sharing and Static Greed Selection [J]. Computer Science, 2020, 47(5): 217-224.
[2] HUANG Guang-qiu, LU Qiu-qin. Vertical Structure Community System Optimization Algorithm [J]. Computer Science, 2020, 47(4): 194-203.
[3] HUO Jiu-yuan, WANG Ye, HU Zhuo-ya. Convergence Analysis of Artificial Bee Colony Algorithm:Combination of Number and Shape [J]. Computer Science, 2018, 45(10): 212-216.
[4] KONG Xiang-yu, LIU San-yang and WANG Zhen. Almost Sure Convergence of Artificial Bee Colony Algorithm:A Martingale Method [J]. Computer Science, 2015, 42(9): 246-248.
[5] ZHU Xu-hui, NI Zhi-wei and CHENG Mei-ying. Self-adaptive Improved Artificial Fish Swarm Algorithm with Changing Step [J]. Computer Science, 2015, 42(2): 210-216.
[6] HAN Li-xia. Novel Genetic Algorithm for Multi-objective Optimization Problem [J]. Computer Science, 2013, 40(Z6): 64-66.
[7] LU Qiu-qin,NIU Qian-qian and HUANG Guang-qiu. Cellular Automata Algorithm for Solving Optimization Problems Based on Memory Principles and its Global Convergence Proof [J]. Computer Science, 2013, 40(4): 249-255.
[8] ZHANG Xiang-song,LIU San-yang. Complementarity Support Vector Machines [J]. Computer Science, 2010, 37(2): 165-166.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!