Improving performance via population growth and local search: The case of the artificial bee colony algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7401 LNCS, Issue , 2012, Pages 85-96

  Aydin, Doǧan ^a   Liao, Tianjun ^b   Montes De Oca, Marco A ^c   Stützle, Thomas ^b  

^a DUMLUPINAR UNIVERSITY   (Turkey)

^b UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

^c UNIVERSITY OF DELAWARE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANT COLONY OPTIMIZATION ALGORITHMS; ARTIFICIAL BEE COLONY ALGORITHMS; BENCH-MARK PROBLEMS; CONTINUOUS OPTIMIZATION; HIGH QUALITY; IMPROVING PERFORMANCE; LOCAL SEARCH; MODIFIED ALGORITHMS; POPULATION GROWTH; POPULATION SIZES;

ALGORITHMS; CONSTRAINED OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO);

POPULATION STATISTICS;

EID: 84870813630     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-35533-2_8     Document Type: Conference Paper

References (22)

1. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE International Conference of Neural Networks, vol. 4, pp. 1942-1948 (1995)
2. Socha, K., Dorigo, M.: Ant colony optimization for continuous domains. European Journal of Operational Research 185, 1155-1173 (2008)
3. Montes de Oca, M.A., Stützle, T., Van den Enden, K., Dorigo, M.: Incremental social learning in particle swarms. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(2), 368-384 (2011)
4. Montes de Oca, M.A., Aydin, D., Stützle, T.: An incremental particle swarm for large-scale optimization problems: An example of tuning-in-the-loop (re)design of optimization algorithms. Soft Computing 15(11), 2233-2255 (2011)
5. Liao, T., Montes de Oca, M.A., Aydin, D., Stützle, T., Dorigo, M.: An Incremental Ant Colony Algorithm with Local Search for Continuous Optimization. In: GECCO 2011, pp. 125-132. ACM Press, New York (2011)
6. Lozano, M., Molina, D., Herrera, F.: Editorial: Scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Computing 15(11), 2085-2087 (2011)
7. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes Universitesi, Computer Engineering Department (2005)
8. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3), 459-471 (2007)
9. Eiben, A.E., Marchiori, E., Valkó, V.A.: Evolutionary Algorithms with On-the-Fly Population Size Adjustment. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN VIII. LNCS, vol. 3242, pp. 41-50. Springer, Heidelberg (2004)
10. Chen, D., Zhao, C.: Particle swarm optimization with adaptive population size and its application. Applied Soft Computing 9(1), 39-48 (2009)
11. Hsieh, S., Sun, T., Liu, C., Tsai, S.: Efficient population utilization strategy for particle swarm optimizer. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39(2), 444-456 (2009)
12. Auger, A., Hansen, N.: A restart CMA evolution strategy with increasing population size. In: CEC 2005, pp. 1769-1776. IEEE Press (2005)
13. Balaprakash, P., Birattari, M., Stützle, T.: Improvement Strategies for the F-Race Algorithm: Sampling Design and Iterative Refinement. In: Bartz-Beielstein, T., Blesa Aguilera, M.J., Blum, C., Naujoks, B., Roli, A., Rudolph, G., Sampels, M. (eds.) HM 2007. LNCS, vol. 4771, pp. 108-122. Springer, Heidelberg (2007)
14. Birattari, M., Yuan, Z., Balaprakash, P., Stützle, T.: F-Race and iterated F-Race: An overview. In: Experimental Methods for the Analysis of Optimization Algorithms, pp. 311-336. Springer, Heidelberg (2010)
15. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341-359 (1997)
16. Eshelman, L., Schaffer, J.: Real-coded genetic algorithms and interval-schemata. Foundations of Genetic Algorithms 2, 187-202 (1993)
17. Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical Report 2005005, Nanyang Technological University (2005)
18. Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214(1), 108-132 (2009)
19. Powell, M.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. The Computer Journal 7(2), 155-162 (1964)
20. Tseng, L., Chen, C.: Multiple trajectory search for large scale global optimization. In: CEC 2008, pp. 3052-3059. IEEE Press, Piscataway (2008)
21. Herrara, F., Lozano, M., Molina, D.: Test suite for the special issue of soft computing on scalability of evolutionary algorithms and other metaheuristics for large scale continuous optimization problems (2010), http://sci2s.ugr.es/eamhco/updated-functions1-19.pdf
22. Diwold, K., Aderhold, A., Scheidler, A., Middendorf, M.: Performance evaluation of artificial bee colony optimization and new selection schemes. Memetic Computing 3(3), 149-162 (2011)