Combined Algorithm for Decision Making

The paper deals with a key problem of development new effective heuristic mechanisms for decision making. As the most promising trend in terms of decision-making theory there are considered principles and rules of objects' behavior in nature. The most interesting are methods and algorithms based on multiagent control and swarm intelligence. The paper builds on a swarm algorithm inspired by bees colony behavior. The authors developed a bioinspired search architecture for decision making which involves a genetic algorithm, evolutionary adaptation and bees algorithm. Also, it is suggested combined algorithm for intelligent decision making.

[1]  Arpan Kumar Kar,et al.  Bio inspired computing - A review of algorithms and scope of applications , 2016, Expert Syst. Appl..

[2]  Sachin S. Sapatnekar,et al.  Handbook of Algorithms for Physical Design Automation , 2008 .

[3]  Andrey A. Legebokov,et al.  Neighborhood research approach in swarm intelligence for solving the optimization problems , 2014, Proceedings of IEEE East-West Design & Test Symposium (EWDTS 2014).

[4]  A. A. Lezhebokov,et al.  Problem-Oriented Algorithms of Solutions Search Based on the Methods of Swarm Intelligence , 2013 .

[5]  Shu-Ping Wan,et al.  A novel method for group decision making with interval-valued Atanassov intuitionistic fuzzy preference relations , 2016, Inf. Sci..

[6]  Daria Zaruba,et al.  Hybrid Bionic Algorithms for Solving Problems of Parametric Optimization , 2013 .

[7]  Evolutionary Algorithm for Extremal Subsets Comprehension in Graphs , 2013 .

[8]  Yaoguang Hu,et al.  Multilevel decision-making: A survey , 2016, Inf. Sci..

[9]  Zhongbo Hu,et al.  Modified differential evolution algorithm with onlooker bee operator for mixed discrete-continuous optimization , 2016, SpringerPlus.

[10]  Bahriye Akay,et al.  Comparisons of metaheuristic algorithms and fitness functions on software test data generation , 2016, Appl. Soft Comput..

[11]  A. A. Lezhebokov,et al.  A New Approach for Software Development in Terms of Problem-Oriented Knowledge Search and Processing , 2016 .

[12]  Y. A. Kravchenko.  The analyses, perspectives and problems of artificial neural networks development , 2002, Proceedings 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS 2002).

[13]  E. V. Kuliev,et al.  Automated formation of the interactive tasks for the computer-aided training , 2015, 2015 9th International Conference on Application of Information and Communication Technologies (AICT).

[14]  Selma Ayse Özel,et al.  A hybrid approach of differential evolution and artificial bee colony for feature selection , 2016, Expert Syst. Appl..