A multi-population cultural algorithm with adaptive diversity preservation and its application in ammonia synthesis process

A multi-population cultural differential evolution (MCDE) algorithm is proposed. Each of the populations is managed by its private cultural differential evolution algorithm, in which a center individual is introduced into the belief space and selection function follows a new method to select the offspring for the next generation. To accelerate the convergence speed, the populations exchange their knowledge with each other every given generations. An adaptive mechanism of population diversity preservation is put forward to prevent the populations from being trapped in local optima. In the adaptive mechanism, the idea of culture fusion between populations is used to know the convergence status, so that the diversity of populations is kept along the evolutionary process. The performance evaluation on MCDE using eleven constrained optimization problems shows that MCDE is a competitive approach. MCDE is further applied to a practical optimization problem in an ammonia synthesis system with the objective to maximize the net value of ammonia. The results achieved by MCDE are compared with those by two traditional differential evolution algorithms, which indicate that MCDE has more excellent performance and better effectiveness.

[1]  Marco Wiering,et al.  Proceedings of IEEE International Conference on Evolutionary Computation , 2013 .

[2]  S. M. Khazraee,et al.  Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution , 2010, Neural Computing and Applications.

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

[4]  Yu Liu,et al.  Center particle swarm optimization , 2007, Neurocomputing.

[5]  Robert G. Reynolds,et al.  Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[6]  Carlos Alberto Ochoa Ortíz Zezzatti,et al.  A Cultural Algorithm for the Urban Public Transportation , 2010, HAIS.

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

[8]  Carlos A. Coello Coello,et al.  Solving timetabling problems using a cultural algorithm , 2011, Appl. Soft Comput..

[9]  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).

[10]  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.

[11]  Vadlamani Ravi,et al.  Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks , 2009, Expert Syst. Appl..

[12]  Robert G. Reynolds,et al.  Knowledge-Based Constrained Function Optimization Using Cultural Algorithms with an Enhanced Social Influence Metaphor , 2011 .

[13]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[14]  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).

[15]  Ling Chen,et al.  Cultural algorithm for minimization of binary decision diagram and its application in crosstalk fault detection , 2010, Int. J. Autom. Comput..

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

[17]  Jun Xiao,et al.  Research of PID parameter optimization based-on cultural based Ant Colony Algorithm for superheated steam temperature , 2010, Proceedings of the 29th Chinese Control Conference.

[18]  Vadlamani Ravi,et al.  An improved differential evolution method for efficient parameter estimation in biofilter modeling , 2006 .

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

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

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

[22]  Leandro dos Santos Coelho,et al.  Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems , 2009, Math. Comput. Simul..

[23]  Abdelaziz Bouroumi,et al.  A multipopulation cultural algorithm using fuzzy clustering , 2007, Appl. Soft Comput..

[24]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[25]  Jian Cheng,et al.  A novel multi-population cultural algorithm adopting knowledge migration , 2011, Soft Comput..

[26]  Zbigniew Michalewicz,et al.  An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms , 1991, ICGA.

[27]  Konstantinos G. Margaritis,et al.  A multipopulation cultural algorithm for the electrical generator scheduling problem , 2002, Math. Comput. Simul..

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

[29]  Allon J. Uhlmann.  Coevolution: genes, culture and human diversity , 1993 .

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