caa a22 4500 60615972X CHVBK 20210128100626.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00521-014-1752-4 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1752-4 Fuzzy adaptive imperialist competitive algorithm for global optimization [Elektronische Daten] [Abdullah Khaled, Seyedmohsen Hosseini] Most of the parameters in meta-heuristic algorithms need precise tuning before applying into the optimization problems. The quality of solutions obtained by meta-heuristic algorithms is highly dependent on how efficiently its parameters are tuned by the users. Setting the parameters is not straightforward, and there is no definitive way to determine their appropriate values other than trial and error, which is an inefficient and time-consuming process. Deviation angle parameter in the imperialist competitive algorithm (ICA) plays an important role as driver of search diversification and intensification. In this paper, we propose a fuzzy adaptive imperialist competitive algorithm (FAICA), where the deviation parameter is adaptively adjusted using a fuzzy controller to optimally control diversification and intensification through the search process. A set of benchmark function tests has been carried out to demonstrate the effectiveness and robustness of the proposed algorithm. The results of experiments are compared with the original ICA and other five well-known algorithms. The experimental results exhibit the merit of achieving balance between intensification and diversification in the search which indicates superiority of FAICA over ICA. The Natural Computing Applications Forum, 2014 Fuzzy adaptive controller nationallicence Fuzzy imperialist competitive algorithm nationallicence ICA nationallicence Deviation parameter nationallicence Khaled Abdullah Influence Health, Birmingham, AL, USA aut Hosseini Seyedmohsen School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, USA aut Neural Computing and Applications Springer London 26/4(2015-05-01), 813-825 0941-0643 26:4<813 2015 26 521 https://doi.org/10.1007/s00521-014-1752-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1752-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Khaled Abdullah Influence Health, Birmingham, AL, USA aut NATIONALLICENCE 700 1- Hosseini Seyedmohsen School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, USA aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/4(2015-05-01), 813-825 0941-0643 26:4<813 2015 26 521