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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Hu, X.; Zhang, J.; Li, Y. (2008)
Publisher: Springer
Languages: English
Types: Article
Subjects: QA75, QA76

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_ARTIFICIALINTELLIGENCE, MathematicsofComputing_NUMERICALANALYSIS
Research into ant colony algorithms for solving continuous optimization problems forms one of the most\ud significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial\ud optimization, they have shown great potential in solving a wide range of optimization problems, including continuous\ud optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for\ud solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore\ud their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed\ud algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is\ud compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions\ud in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • domains. Eur. J. Oper. Res., 2008, 185(3): 1155{1173. sity, Guangzhou, China. She is cur[20] Pourtakdoust S H, Nobahari H. An extension of ant colony rently a Ph.D. candidate majored
    • ANTS 2004, Brussels, Belgium, LNCS 3172, 2004, pp.294{ in computer application and tech-
    • 301. nology in Sun Yat-Sen University. [21] Kong M, Tian P. A binary ant colony optimization for the Her research interests include arti¯-
    • Security (CIS'05), Xi'an, China, LNAI 3801, 2005, pp.682{ biological information.
    • 687. [22] Kong M, Tian P. A direct application of ant colony optimiza- Jun Zhang received the Ph.D.
    • In Proc. ANTS 2006, Brussels, Belgium, LNCS 4150, 2006, degree in electrical engineering from
    • pp.324{331. City University of Hong Kong, in [23] Chen L, Shen J, Qin L, Chen H J. An improved ant colony 2002. From 2003 to 2004, he was a
    • algorithm in continuous optimization. Journal of Systems Brain Korean 21 Postdoctoral Fel-
    • Science and Systems Engineering, 2003, 12(2): 224{235. low in the Department of EECS, [24] Dr¶eo J, Siarry P. An ant colony algorithm aimed at dynamic Korea Advanced Institute of Science
    • continuous optimization. Appl. Math. Comput., 2006, 181: and Technology (KAIST). Since
    • 2004, he has been with the Sun Yat[25] Feng Y J, Feng Z R. An immunity-based ant system for con-
    • and Cybernetics, Shanghai, August 26{29, 2004, pp.1050{ Computer Science. He has authored three research book
    • 1054. chapters and over 50 refereed technical papers in his re[26] Shelokar P S, Siarry P, Jayaraman V K, Kulkarni B D. Parti- search areas. His research interests include genetic algo-
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    • 9(Suppl.): 128{139. Yun Li received the B.S. de[28] Bush K A. Orthogonal arrays [Dissertation]. University of gree in radio electronics science
    • North Carolina, Chapel Hill, 1950. from Sichuan University, Chengdu, [29] Math Stat Res Group, Chinese Acad Sci. Orthogonal De- China, in 1984, an M.Sc. de-
    • sign. Bejing: People Education Pub., 1975. (in Chinese) gree in electronic engineering from [30] Fang K T, Wang Y. Number-Theoretic Methods in Statis- University of Electronic Science
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    • Theory and Applications. New York: Springer-Verlag, 1999. Chengdu, in 1987, and a Ph.D. de[32] Nathanson M B. Elementary Methods in Number Theory. gree in computing and control en-
    • New York: Springer-Verlag, 2000. gineering from University of Strath[33] Zhang Q, Leung Y W. An orthogonal genetic algorithm for clyde, Glasgow, U.K., in 1990. From 1989 to 1990, he
    • Computation, 1999, 3(1): 53{62. for Industrial Systems and Control Limited, Glasgow, U.K. [34] Leung Y W, Wang W. An orthogonal genetic algorithm with He became a lecturer at the University of Glasgow in 1991.
    • Evol. Comput., 2001, 5(1): 41{53. In 2002, he served as a visiting professor at Kumamoto [35] Ho S Y, Chen J H. A genetic-based systematic reasoning University, Japan. He is currently a senior lecturer at Uni-
    • orthogonal array crossover. In Proc. the Fourth Internal 1996, he independently invented the \inde¯nite scatter1) /* Initialization phase */
    • countE := 0 2) /* Orthogonal exploration phase */
    • For each region j do visitj := 0 End-for
    • For k := 1 to m do
    • Choose the next region j according to (1) (2)
    • visitj := visitj + 1
    • countE := countE + 1
    • countE := 0
    • End-if 3) /* Global modulation phase */
    • countG := 0 SjR := ; Sj0R = ; For j := 1 to ¹ do
    • rank j := 0
    • Add region j to SjR End-for For i :=1 to à £ ¹ do
    • Find the region j with the minimum value satis¯ed 1) f10 and f11
    • 1 yi = 1 + (xi + 1);
    • 8 p(xi ¡ a)j;
    • > u(xi; a; p; j) = < 0; The value of p is equal to 5, 7, and 10 respectively from
  • No related research data.
  • Discovered through pilot similarity algorithms. Send us your feedback.

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