Abstract
A novel cultural differential evolution algorithm with multiple populations (MCDE) is proposed. The single individual in each population is affected by the situational and normative knowledge from belief space simultaneously. The populations communicate with each other following a rule of knowledge exchange, which helps to enhance the search rate of evolution. The concept of culture fusion is introduced to develop an adaptive mechanism of preserving the population diversity. The mechanism ensures that populations are diverse along the whole evolution and excellent candidate solutions are not rejected. The performance of MCDE algorithm is validated by typical constrained optimization problems. Finally, MCDE is applied to maximizing the net value of ammonia in an ammonia synthesis loop. The results indicate that the proposed algorithm has the potential to be used in other problems.
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References
Durham, W.: Co-evolution: Genes, Culture, and Human Diversity. Stanford University Press, Stanford (1994)
Reynolds, R.G.: An Introduction to Cultural Algorithm. In: Proceedings of the 3rd Annual Conference on Evolutionary Programming, pp. 131–139. World Scientific, Singapore (1994)
Reynolds, R.G., Peng, B., Brewster, J.J.: Cultural Swarms: Knowledge-driven Problem Solving in Social Systems. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 3589–3594. IEEE Press, New York (2003)
Gao, F., Cui, G., Liu, H.: Integration of Genetic Algorithm and Cultural Algorithms for Constrained Optimization. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4234, pp. 817–825. Springer, Heidelberg (2006)
Lin, C., Chen, C., Lin, C.: A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications. IEEE Trans. Syst. Man. Cy. C 39, 55–68 (2009)
Ricardo, L.B., Carlos, A.C.C.: A Cultural Algorithm with Differential Evolution to Solve Constrained Optimization Problems. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 881–890. Springer, Heidelberg (2004)
Jin, X., Reynolds, R.G.: Date Mining using Cultural Algorithms and Regional Schemata. In: 14th IEEE International Conference on Tools with Artificial Intelligence, pp. 33–44. IEEE Press, New York (2002)
Huang, H., Gu, X.: Neural Network based on Cultural Algorithms and Its Application on Modeling. Control and Decision 23, 477–480 (2008) (in Chinese)
Yuan, X., Nie, H., He, L., Li, C., Zhang, Y.: A Cultural Algorithm for Scheduling of Hydro Producer in the Power Market. In: Second International Conference on Genetic and Evolutionary Computing, pp. 364–367. IEEE Press, New York (2008)
Storn, R., Price, K.: Differential Evolution–a Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical report, International Computer Science Institute 8, 22–25 (1995)
Koziel, S., Michalewicz, Z.: Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization. Evol. Comput. 7, 19–44 (1999)
Zangwill, W.I.: Nonlinear Programming via Penalty Functions. Management Science 13, 344–358 (1967)
Runarsson, T.P., Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Trans. Evol. Comput. 4, 284–294 (2000)
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Xu, W., Zhang, L., Gu, X. (2010). A Novel Cultural Algorithm and Its Application to the Constrained Optimization in Ammonia Synthesis. In: Li, K., Li, X., Ma, S., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Communications in Computer and Information Science, vol 98. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15859-9_8
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DOI: https://doi.org/10.1007/978-3-642-15859-9_8
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