A New OFNBee Method as an Example of Fuzzy Observance Applied for ABC Optimization

The chapter includes a hybrid concept combining bee colony optimization with the application of Ordered Fuzzy Numbers. This is another research, after the OFNAnt method, prepared in AIRlab - Artificial Intelligence and Robotics Laboratory at Kazimierz Wielki University in Bydgoszcz, in which authors enriched metaheuristics by implementing the arithmetics of Ordered Fuzzy Numbers (OFNs). Applied fuzzy observation enabled very faithful modeling of the navigation mechanism used by bees when orienting with reference to the position of the sun. Experiments aimed at verification of the developed concept have been carried out on a set of several commonly known benchmarks. The preliminary results of experiments allow us to nurture grounded hope that further modifications of the metaheuristics using OFN arithmetics shall enable smooth control of the optimization criteria of the tested phenomena.

[1]  Piotr Prokopowicz.  The Directed Inference for the Kosinski's Fuzzy Number Model , 2015, AECIA.

[2]  Leszek Rutkowski,et al.  Neural Networks and Soft Computing , 2003 .

[3]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[4]  Jacek Czerniak,et al.  Application of Ordered Fuzzy Numbers in a New OFNAnt Algorithm Based on Ant Colony Optimization , 2014, BDAS.

[5]  Magdalena Kacprzak,et al.  On lattice structure and implications on ordered fuzzy numbers , 2011, EUSFLAT Conf..

[6]  Piotr Prokopowicz,et al.  Defuzzification Functionals of Ordered Fuzzy Numbers , 2013, IEEE Transactions on Fuzzy Systems.

[7]  Rafal A. Angryk,et al.  Heuristic algorithm for interpretation of multi-valued attributes in similarity-based fuzzy relational databases , 2010, Int. J. Approx. Reason..

[8]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[9]  Jacek Czerniak,et al.  Representation of a trend in OFN during fuzzy observance of the water level from the Crisis control center , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[10]  Jacek Koronacki.  Stochastic approximation. II. Optimization methods with constraints , 1977 .

[11]  Witold Kosinski,et al.  Ordered fuzzy numbers in financial stock and accounting problems , 2013, 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS).

[12]  Witold Kosiński,et al.  Fuzzy reals and their quotient space with algebraic operations , 1993 .

[13]  Hoon Hong.  Special Issue Editorial: Computational Quantifier Elimination , 1993, Comput. J..

[14]  Jacek Czerniak,et al.  Quality of Services Method as a DDoS Protection Tool , 2014, IEEE Conf. on Intelligent Systems.

[15]  Jacek Czerniak,et al.  A Proposal of the New owlANT Method for Determining the Distance between Terms in Ontology , 2014, IEEE Conf. on Intelligent Systems.

[16]  Marek Macko,et al.  The CutMAG as a New Hybrid Method for Multi-edge Grinder Design Optimisation , 2015, IWIFSGN@FQAS.

[17]  Piotr Prokopowicz.  Adaptation of Rules in the Fuzzy Control System Using the Arithmetic of Ordered Fuzzy Numbers , 2008, ICAISC.

[18]  D. Pham,et al.  THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .

[19]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[20]  W. Kosiński,et al.  Algebra liczb rozmytych , 2004 .

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

[22]  Magdalena Kacprzak,et al.  Metasets and Opinion Mining in New Decision Support System , 2015, ICAISC.

[23]  Dominik Slezak,et al.  Fuzzy Reals with Algebraic Operations: Algorithmic Approach , 2002, Intelligent Information Systems.

[24]  Tadeusz Burczynski,et al.  Financial Fuzzy Time Series Models Based on Ordered Fuzzy Numbers , 2013, Time Series Analysis, Modeling and Applications.

[25]  Magdalena Kacprzak,et al.  Implications on Ordered Fuzzy Numbers and Fuzzy Sets of Type Two , 2012, ICAISC.

[26]  Maria Ganzha,et al.  Developing intelligent bots for the Diplomacy game , 2011, 2011 Federated Conference on Computer Science and Information Systems (FedCSIS).

[27]  Marcin Paprzycki,et al.  New proposed implementation of ABC method to optimization of Water Capsule Flight , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[28]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[29]  Dariusz Kacprzak,et al.  Optimizing of a Company’s Cost under Fuzzy Data and Optimal Orders Under Dynamic Conditions , 2014 .

[30]  Piotr Prokopowicz,et al.  Methods based on ordered fuzzy numbers used in fuzzy control , 2005, Proceedings of the Fifth International Workshop on Robot Motion and Control, 2005. RoMoCo '05..

[31]  Piotr Prokopowicz,et al.  Analysis of the changes in processes using the Kosinski's Fuzzy Numbers , 2016, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS).

[32]  Dominik Slezak,et al.  Calculus with Fuzzy Numbers , 2004, IMTCI.

[33]  Jacek Czerniak,et al.  Proposed CAEva Simulation Method for Evacuation of People from a Buildings on Fire , 2015, IWIFSGN@FQAS.

[34]  Dervis Karaboga,et al.  A survey on the applications of artificial bee colony in signal, image, and video processing , 2015, Signal, Image and Video Processing.

[35]  M. Lindauer,et al.  Communication in Swarm-Bees Searching for a New Home , 1957, Nature.

[36]  Piotr Prokopowicz,et al.  Flexible and Simple Methods of Calculations on Fuzzy Numbers with the Ordered Fuzzy Numbers Model , 2013, ICAISC.

[37]  Dominik Ślęzak,et al.  On Algebraic Operations on Fuzzy Reals , 2003 .

[38]  Magdalena Kacprzak,et al.  New Approach to Decision Making , 2015, AECIA.