[1]QIAN Shu-qu,ZHANG Zhu-hong.Dynamic multiobjective immune optimization algorithm 〖JZ〗and performance test[J].CAAI Transactions on Intelligent Systems,2007,2(5):68-77.
Copy

Dynamic multiobjective immune optimization algorithm 〖JZ〗and performance test

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 2 Number of periods: 2007 5 Page number: 68-77 Column: 学术论文—人工智能基础 Public date: 2007-10-25
Title:
Dynamic multiobjective immune optimization algorithm 〖JZ〗and performance test
Author(s):
QIAN Shu-qu1; 2;  ZHANG Zhu-hong1
1．College of Science, Guizhou University, Guizhou 550025， China;
2．Department of Mathematics, Anshun College， Anshun 561000， China
Keywords:
dynamic multiobjective optimization;  timevarying Pareto front;  en vironment tracking;  adaptive neighborhood;  immune algorithm.
CLC:
TP301.6
DOI:
-
Abstract:
A dynamic multiobjective immune optimization algorithm suitable for d ynamic multiobjective optimization problems is proposed based on the functions of adaptive learning, immune memory, antibody diversity and dynamic balance main tenance, etc. In the design of the algorithm, the scheme of antibody affinity w a s designed based on the locations of adaptiveneighborhood and antibody; ant i bodies participating in evolution were selected by Pareto dominance. In order to enhance the average affinity of the population, clonal proliferation and adapti ve Gaussian mutation were adopted to evolve excellent antibodies. Furthermore, t he average linkage method and several functions of immune memory and dynamic bal ance maintenance were used to design environmental recognition rules and the mem ory pool. The proposed algorithm was compared against several popular multiobj e ctive algorithms by means of three different kinds of dynamic multiobjective b e nchmark problems. Simulations show that the algorithm has great potential in s olving dynamic multiobjective optimization problems. 
References:
［1］BINGUL Z. Adaptive genetic algorithms applied to dynamic multiobjective pr oblems［J］. Applied Soft Computing，7(2007) 791-799.
［2］FARINA M, DEB K, AMATO P. Dynamic multiobjective optimization problems: te st case, approximations, and applications［J］. IEEE Transactions on Evolutionary Computation, 2004, 8(5): 425-442.
［3］ZITZLER E, LAUMANNS M, THIELE L. Speaii: improving the strength pareto evo lu tionary algorithm［A］. Evolutionary Methods for Design, Optimization and Contro l with Applications to Industrial Problems［C］. Athens, Greece, 2001.
［4］DEB K, AGRAWAL S, PRATAP A, et al. A fast elitist nondominated sorting gen et ic algorithm for multiobjective optimization: NSGAII［J］. IEEE Transactions o n Evolutionary Computation, 2002, 6(2): 182-197.
［5］JIN Y, BRANKE J. Evolutionary optimization in uncertain environmentsA sur vey［J］. IEEE Transactions on Evolutionary Computation, 2005, 9(3): 303-317.
［6］AMATO P, FARINA M. An alifeinspired evolutionary algorithm for dynamic mu ltiobjective optimization problems［A］. In WSC［C］.［S.l.］， 2003.
［7］HATZAKIS I, WALLACE D. Dynamic multiobjective optimization with evolutiona ry algorithm: a forwardlooking approach［A］. GECCO’06［C］ . Washington, USA, 2 006.
［8］DEB K, UDAYA B R N， KARTHIK S. Dynamic multiobjective optimiz ati on and decisionmaking using modified NSGAII: a case study on hydrothermal po we r scheduling biobjective optimization problems［R］. KanGAL Report, 2006.
［9］COELLO C A, CRUZ Cort N. Solving multiobjective optimization problems usin g an aritificial immune system［J］. Genetic Programming and Evolvable Machine, 20 05, 6 :163-190.
［10］SHANG R H, JIAO L C, GONG M G, et al. Clonal selection algorithm for dyn am ic multiobjective optimization［A］. CIS 2005［C］. Berlin: SpringerVerlag, 20 05.
［11］黄席越, 张著洪, 何传江，等. 现代智能算法理论及应用［M］. 北京: 科学出版社 , 2005.
Similar References:

Memo

-

Last Update: 2009-05-08

Copyright © CAAI Transactions on Intelligent Systems