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Abstrakty
This article provides, first, a review of applications of the ecosystem idea in different computational intelligence methods. The article presents the bases of ecosystem operation and a new concept for modelling the phenomena occurring in an ecosystem, with the aim of using these for optimization purposes. The author’s original form of the Artificial Ecosystem Algorithm (AEA) and its constituent parts are presented. The construction of the proposed algorithm was dedicated for continuous optimisation. The operation of the Artificial Ecosystem Algorithm is also compared with an Evolutionary Algorithm and PSO for six test functions for various numbers of variables. Conclusions concerning operation, structure and complexity of AEA are provided at the end.
Czasopismo
Rocznik
Tom
Strony
5--36
Opis fizyczny
Bibliogr. 41 poz., rys., tab.
Twórcy
autor
- Warsaw University of Technology, Institute of Electrical Power Engineering Koszykowa 75, 00-662
Bibliografia
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- [2] BACZYNSKI D. (2013) Metody inteligencji obliczeniowej w elektroenergetyce (in Polish: Computational Intelligence Methods in Electrical Power Engineering), Monograph, Warsaw University of Technology Scientific Works, Elektryka, issue 145, ISSN 0137-2319, WUT Publishing Office, Warsaw.
- [3] BINITHA, S. and SIVA SATHYA, S. (2012) A Survey of Bio inspired Optimization Algorithms. International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, 2, 2, May 2012.
- [4] BONABEAU, E., DORIGO, M., THERAULAZ, G. (1999) Swarm Intelligence, From Natural to Artificial Systems. Oxford University Press.
- [5] BRATTON, D. and KENNEDY, J. (2007) Defining a Standard for Particle Swarm Optimization. Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS 2007).
- [6] BREMERMANN, H. (1974). Chemotaxis and optimization. Journal of Franklin Institute 297, 397–404. BRISCOE, G., SADEDIN, S., DE WILDE, P. (2011) Digital Ecosystems: Ecosystem–Oriented Architectures. Natural Computing 10: 1143–1194, Springer.
- [7] BRISCOE, G., SADEDIN, S., PAPERIN, G. (2007) Biology of Applied Digital Ecosystems. Digital EcoSystems and Technologies Conference, 2007. DEST ’07. Inaugural IEEE-IES, 458-463, 21-23 Feb. 2007.
- [8] CHEN, H. and YUNLONG, Z. (2008) Optimization Based on Symbiotic Multispecies Coevolution. Journal on Applied Mathematics and Computation, 205.
- [9] CHENG, M.Y. and PRAYOGO, D. (2014) Symbiotic Organisms Search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98–112.
- [10] DE BOER, F. K. and HOGEWEG, P. (2012) Co-evolution and ecosystem based problem solving. Ecological Informatics 9, 47–58.
- [11] DE CASTRO, L.N. and TIMMIS, J. (2003) Artificial immune systems as a novel soft computing paradigm. Soft Computing 7, 526–544, SpringerVerlag
- [12] DORIGO, M., BIRATTARI, M. and STUTZLE, T. (2006) Ant colony optimization. Computational Intelligence Magazine, IEEE, 1, 4, 28-39, Nov. 2006
- [13] EUSUFF, M.M. and LANSEY, K.E. (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management 129 (3), 210–225.
- [14] FENG, X., LAU, F.C.M. and GAO, D. (2009) A new bio-inspired approach to the traveling salesman problem. Complex Sciences, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, February, 5, 310–1321, Springer, Berlin–Heidelberg.
- [15] GEEM, Z.W., KIM, J.-H. and LOGANATHAN, G.V. (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68.
- [16] GLOVER, F. (1990) Tabu Search - part II. ORSA Journal of Computing, 2(1).
- [17] HAI-FEI, Yu and DING-WEI, Wang (2006) Design and Analysis of Food-Chain Algorithm. Computational Intelligence and Security, 2006 International Conference on, 1, 453-456, Nov. 2006.
- [18] HAVENS, T., SPAIN, C., SALMON, N. and KELLER, J. (2008) Roach infestation optimization. IEEE Swarm Intelligence Symposium, September, 1–7.
- [19] HERTZ, J., KROGH, A. and PALMER, R. (1991) Introduction to the Theory of Neural Computation. Addison Wesley, Amsterdam.
- [20] KENNEDY, J. and EBERHART, R. (1995) Particle swarm optimization. Neural Networks, Proceedings, IEEE International Conference on, 4, 1942 1948, Nov/Dec 1995.
- [21] KIRKPATRICK, S., GELATT, C.D. and VECCHI, M. P. (1983) Optimization by simulated annealing. Science, 220 (4598), 671–680.
- [22] KRISHNANAND, K.N. and GHOSE, D. (2005) Detection of multiple source locations using a glowwormmetaphor with applications to collective robotics. Swarm Intelligence Symposium, SIS 2005. Proceedings 2005 IEEE, 84- 91, 8-10 June 2005
- [23] LAFUSA, A. (2007) Studying Long-term Evolution with Artificial Life. Artificial Life, 2007. ALIFE ’07. IEEE Symposium on, 5-22, 1-5 April 2007.
- [24] MEHRAB IAN, A.R. and LUCAS, C. (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics 1, 355– 366.
- [25] MICHALEWICZ, Z. (1992) Genetic algorithms + data structures = evolution programs. Springer-Verlag, Berlin Heidelberg.
- [26] MONISMITH, D. and MAYFIELD, B. (2008) Slime mold as a model for numerical optimization. IEEE Swarm Intelligence Symposium, 1–8.
- [27] MOSCATO, P. (1989) On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical report, California Institute of Technology.
- [28] PICHLER, P.P. and CANAMERO, L. (2007) An Evolving Ecosystems Approach to Generating Complex Agent Behaviour. Artificial Life, ALIFE ’07. IEEE Symposium on, 303-310, 1-5 April 2007.
- [29] RABANAL, P., RODR´IGUEZ, I. and RUBIO, F. (2007) Using river formation dynamics to design heuristic algorithms. Unconventional Computation, LNCS 4618, 163–177. Springer.
- [30] REYNOLDS, R. G. (1994) An Introduction to Cultural Algorithms. Proceedings of the Third Annual Conference on Evolutionary Programming, February 24-26, 1994, San Diego, California, 131-139.
- [31] SAGOFF, M. (2003) The plaza and the pendulum: two concepts of ecological science. Biology and Philosophy 18(4), Springer Netherlands.
- [32] SHAH HOSSEINI, H. (2007) Problem solving by intelligent water drops. Evolutionary Computation, CEC 2007. IEEE Congress on, 3226-3231, 25-28 Sept. 2007.
- [33] SIMON, D. (2008) Biogeography-Based Optimization. Evolutionary Computation, IEEE Transactions on, 12, 6, 702-713, Dec. 2008.
- [34] TZIMA, F.A., SYMEONIDIS, A.L., MITKAS, P.A. (2007) Symbiosis: Using Predator-Prey Games as a Test Bed for Studying Competitive Coevolution. Integration of Knowledge Intensive Multi-Agent Systems, KIMAS 2007. International Conference on, 115-120, April 30 2007-May 3 2007.
- [35] ULANOWICZ, R. (2000) Growth and Development, Ecosystems Phenomenology. toExcel/iUniverse 2000
- [36] VULLI, S.S. and AGARWAL, S. (2008) Individual-Based Artificial Ecosystems for Design and Optimization. GECCO’08, July 12–16, 2008, Atlanta, Georgia, USA.
- [37] WEINER, J. (2012) Zycie i ewolucja biosfery (in Polish: Life and the Evolution of the Biosphere). Wydawnictwo Naukowe PWN, Warszawa.
- [38] YANG, X.S. (2010a) Nature-Inspired Metaheuristic Algorithms. Luniver Press.
- [39] YANG, X.S. (2010b) Test problems in optimization. Engineering Optimization: An Introduction with Metaheuristic Applications (Xin-She Yang, ed.), John Wiley & Sons.
- [40] YANG, X.S. and DEB, S. (2009) Cuckoo Search via L´evy flights. Nature & Biologically Inspired Computing, NaBIC 2009. World Congress on, 210214, 9-11 Dec. 2009.
- [41] ZADEH, L.A. (1965) Fuzzy sets. Information and Control, 8, 338-353.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-db73c3e5-dd9c-4eff-807d-f3f839a5a245