A Novel Cultural Algorithm and Its Application to the Constrained Optimization in Ammonia Synthesis

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.

[1]  Carlos A. Coello Coello,et al.  A Cultural Algorithm with Differential Evolution to Solve Constrained Optimization Problems , 2004, IBERAMIA.

[2]  M. Ghiselin,et al.  Coevolution: Genes, Culture, and Human Diversity , 1991, Politics and the Life Sciences.

[3]  Robert G. Reynolds,et al.  Cultural swarms: knowledge-driven problem solving in social systems , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[4]  Gu Xing-sheng Neural network based on cultural algorithms and its application on modeling , 2008 .

[5]  R. Storn,et al.  Differential evolution a simple and efficient adaptive scheme for global optimization over continu , 1997 .

[6]  Chin-Teng Lin,et al.  A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks and Its Prediction Applications , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Robert G. Reynolds,et al.  Data mining using cultural algorithms and regional schemata , 2002, 14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings..

[8]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[9]  Xiaohui Yuan,et al.  A Cultural Algorithm for Scheduling of Hydro Producer in the Power Market , 2008, 2008 Second International Conference on Genetic and Evolutionary Computing.

[10]  W. Zangwill Non-Linear Programming Via Penalty Functions , 1967 .

[11]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization , 1999, Evolutionary Computation.

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  Hongwei Liu,et al.  Integration of Genetic Algorithm and Cultural Algorithms for Constrained Optimization , 2006, ICONIP.

[14]  Jesus A. Gonzalez,et al.  Advances in Artificial Intelligence – IBERAMIA 2004 , 2004, Lecture Notes in Computer Science.