A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning

This work analyses the performance of three different population-based metaheuristic approaches applied to Fuzzy cognitive maps (FCM) learning in qualitative control of processes. Fuzzy cognitive maps permit to include the previous specialist knowledge in the control rule. Particularly, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) are considered for obtaining appropriate weight matrices for learning the FCM. A statistical convergence analysis within 10000 simulations of each algorithm is presented. In order to validate the proposed approach, two industrial control process problems previously described in the literature are considered in this work.

[1]  Bart Kosko,et al.  Virtual Worlds as Fuzzy Cognitive Maps , 1993, Presence: Teleoperators & Virtual Environments.

[2]  Chrysostomos D. Stylios,et al.  Active Hebbian learning algorithm to train fuzzy cognitive maps , 2004, Int. J. Approx. Reason..

[3]  Andreas S. Andreou,et al.  Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive maps , 2005, Soft Comput..

[4]  Witold Pedrycz,et al.  Genetic learning of fuzzy cognitive maps , 2005, Fuzzy Sets Syst..

[5]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[6]  Voula C. Georgopoulos,et al.  Fuzzy cognitive map architectures for medical decision support systems , 2008, Appl. Soft Comput..

[7]  Michael N. Vrahatis,et al.  A first study of fuzzy cognitive maps learning using particle swarm optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[8]  Adil Baykasoglu,et al.  Training Fuzzy Cognitive Maps via Extended Great Deluge Algorithm with applications , 2011, Comput. Ind..

[9]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[10]  Karl Perusich,et al.  Fuzzy cognitive maps for policy analysis , 1996, 1996 International Symposium on Technology and Society Technical Expertise and Public Decisions. Proceedings.

[11]  Rod Taber,et al.  Knowledge processing with Fuzzy Cognitive Maps , 1991 .

[12]  R. Axelrod.  Structure of decision : the cognitive maps of political elites , 2015 .

[13]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[14]  Chrysostomos D. Stylios,et al.  An integrated two-level hierarchical system for decision making in radiation therapy based on fuzzy cognitive maps , 2003, IEEE Transactions on Biomedical Engineering.

[15]  Zhu Yanchun,et al.  An Integrated Framework for Learning Fuzzy Cognitive Map using RCGA and NHL Algorithm , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[16]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[17]  Mostafa Jafari,et al.  Learning FCM by Tabu Search , 2007 .

[18]  Bart Kosko,et al.  Fuzzy Engineering , 1996 .

[19]  Michael N. Vrahatis,et al.  Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization , 2005, Journal of Intelligent Information Systems.

[20]  Jose L. Salmeron,et al.  A Review of Fuzzy Cognitive Maps Research During the Last Decade , 2013, IEEE Transactions on Fuzzy Systems.

[21]  Elpiniki I. Papageorgiou,et al.  Learning Algorithms for Fuzzy Cognitive Maps—A Review Study , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[22]  Elpiniki I. Papageorgiou,et al.  A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps , 2005, Appl. Soft Comput..

[23]  Dimitris E. Koulouriotis,et al.  Comparing simulated annealing and genetic algorithm in learning FCM , 2007, Appl. Math. Comput..

[24]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[25]  Michael Glykas.  Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications , 2010 .