Multi-Swarm That Learn

• Autorzy: Trojanowski K.

Warianty tytułu

PL

Uczący się wielorój

• Języki publikacji: EN

Abstrakty

EN

In this paper a dynamic optimization with particle swarm approach using two different memory mechanisms is studied. One of them is based on the idea of storing explicit solutions in memory structures while the other applies one-pass clustering algorithm to build clusters containing search experiences. Both mechanisms have been experimentally verified and their advantages and disadvantages in application for different types of testing environments have been discussed.

PL

Artykuł zawiera wyniki badań dwóch mechanizmów pamięciowych stosowanych w roju cząsteczek do optymalizacji dynamicznej. Jeden z nich jest oparty na zasadzie gromadzenie gotowych rozwiązań w strukturach pamięci, natomiast drugi stosuje jednoprzejściowy algorytm do budowy klastrów, w których mogłyby być przechowywane doświadczenia zdobywane w trakcie procesu szukania. Obydwa mechanizmy zostały zweryfikowane w badaniach eksperymentalnych a ich wady i zalety objawiające się w zastosowaniach do różnych typów zadań zostały omówione.

Słowa kluczowe

EN

particle swarm optimization (PSO); multi-swarm; dynamic optimization; memory; clusters; clustering evolving data streams; quantum particles

PL

optymalizacja rojem cząsteczek; wielorój; optymalizacja dynamiczna; pamięć; klastry; klasteryzacja strumieni danych; cząsteczki kwantowe

• Wydawca: Instytut Podstaw Informatyki PAN
• Czasopismo: Prace Instytutu Podstaw Informatyki Polskiej Akademii Nauk
• Rocznik: 2009
• Tom: Nr 1015
• Strony: 1--24
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Trojanowski K.

• Instytut Podstaw Informatyki PAN 01-237 Warszawa, Ordona 21 Polska,

Bibliografia

• [1] C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data streams. In VLDB 2003: Proceedings of 29th International Conference on Very Large Data Bases, September 9-12, 2003, Berlin, Germany, pages 81-92. Morgan Kaufmann Publishers, 2003.
• [2] T. Blackwell and J. Branke. Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans. Evol. Comput., 10(4) :459 -472, 2006.
• [3] T. Blackwell and J. Branke. Multi-swarm optimization in dynamic environments. In Applications of Evolutionary Computing, Evo Workshops 2004, volume 3005 of LNCS, pages 489-500. Springer, 2004.
• [4] J. Branke. Memory enhanced evolutionary algorithms for changing optimization problems. In Congress on Evolutionary Computation, Volume 3, pages 1875-1882. IEEE Press, Piscataway, NJ, 1999.
• [5] J. Branke. Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, 2002.
• [6] J. M. Chambers, C. L. Mallows, and B. W. Stuck. A method for simulating stable random variables. J. Amer. Statist. Assoc., 71 (354):340-344, 1976.
• [7] M. Clerc and J. Kennedy. The particle swarm-explosion, stability, and convergence in a multi-dimensional complex space. IEEE Trans. Evol. Comput., 6(l):58-73, 2002.
• [8] J. Kenendy and R. C. Eberhart. Particle swarm optimization. In IEEE Int. Conf. on Neural Networks (ICNN'95), volume 4, pages 1942-1948. IEEE Service Center, Piscataway, NJ, 1995.
• [9] X. Li. Adaptively choosing neighborhood bests in a particle swarm optimizer for multimodal function optimization. In GECCO 2004: Conf. on Genetic and Evolutionary Computation, volume 3102 of LNCS, pages 105-116. Springer, 2004.
• [10] X. Li, J. Branke, and T. Blackwell. Particle swarm with speciation and adaptation in a dynamic environment. In GECCO 2006: Conf. on Genetic and Evolutionary Computation, pages 51— 58. ACM Press, 2006.
• [11] H. Lu and W. Chen. Dynamic-objective particle swarm optimization for constrained optimization problems. J. Comb. optim., 12 (4):409-419, 2006.
• [12] N. Mori, S. Imanishi, H. Kita, and Y. Nishikawa. Adaptation to changing emdronments by means of the memory based thermodynamical genetic algorithm. In 7th Int. Conf. on Genetic Algorithm, pages 299-306. Morgan Kaufman, 1997.
• [13] A. E. Muňoz Zavala, A. H. Aguirre, and E. R. Villa Diharce. Constrained optimization via particle evolutionary swarm optimization algorithm (PESO). In H.-G. Beyer et al., editors, GECCO 2005: Proc. Conference on Genetic and Evolutionary Computation, volume l, pages 209-216. ACM Press, 2005.
• [14] D. Parrot and X. Li. Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans. Evol. Comput, 10(4):440 - 458, 2006.
• [15] G. T. Pulido ancl C. A. C. Coello. A constraint handling mechanism for particle swarm optimization. In Proc. Congress on Evolutionary Computation, volume 2, pages 1396-1403. IEEE, 2004.
• [16] K. Trojanowski. Adaptive non-uniform distribution of quantum particles in maso. In X. Li et al., editors, SEAL 2008, volume 5361 of LNCS, pages 91-100. Springer, 2008.
• [17] K. Trojanowski and Z. Michalewicz. Searching for optima in non-stationary environments. In Congress on Evolutionary Computation, volume 3, pages 1843-1850. IEEE Press, Piscataway, NJ, 1999.
• [18] K. Trojanowski and S. T. Wierzchoń. Searching for memory in artificial immune system. In IIS 2002: 11th Int. Symposium on Intelligent Information Systems, volume 17 of Adv. in Soft Computing. pages 175-183. Physica/Springer, 2002.
• [19] K. Trojanowski and S. T. Wierzchoń. Memory management in artificial immune system. In 6th Int. Conf. on Neural Networks and Soft Computing ICNNSC 2002, volume 19 of Adv. in Soft Computing, pages 650-655. Physica/Springer, 2002.
• [20] K. Trojanowski and S. T. Wierzchoń. Control of immune memory in artificial immune system. In WAE 2002: 3rd National Workshop on Evolutionary Computation and Global Optimisation, pages 111— 118. Warsaw Univ. of Technology Publishing House, 2002.
• [21] K. Trojanowski, Z. Michalewicz, and J. Xiao. Adding memory to the evolutionary planner/navigator. In 4th IEEE Int. Congress on Evolutionary Computation, pages 483-487. IEEE Publishing, 1997.
• [22] T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An efficient clustering method for very large databases. In ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pages 103-114, 1996.