Ensemble centralities based adaptive Artificial Bee algorithm

A survey on evolutionary algorithms dynamics and its complexity – Mutual relations, past, present and future

“…Analysis on complex network [51] and its use in SEA performance control [130] is an alternative way how algorithm dynamics can be analyzed by means of different mathematical tools and can give interesting observations and interpretations. However, it can be expanded further, as described in the previous section via CML systems.…”

“…The analysis of algorithm dynamics, based on previous visualizations and conversions (complex network, CML system), can be done as an analysis of complex network and its interpretation in an inverse way, back to the used algorithm dynamics, as reported in [51,[127][128][129][130] where basic analysis of complex network (generated by SEA) attributes like degree centrality, closeness centrality, betweenness centrality, page rank centrality, mean neighbor degree, community is done among the others. An example of already done initial analysis of complex network generated by algorithm dynamics, see Figs.…”

“…An example of already done initial analysis of complex network generated by algorithm dynamics, see Figs. 2 and 6, and where [51] basic attributes of complex network are interpreted and reported in the SEA terminology as well as in [130,132] where it was used in EAs performance improvements.…”

Study on the Time Development of Complex Network for Metaheuristic

“…These features give a clear description of the population during evaluation and can be utilized for adaptive population and parameter control during the run of EA's. Initial studies [1][2][3] giving the possibilities of transferring the population dynamics into complex networks were followed by successful adaptation and control of EA's during the run through the complex networks framework [4][5][6].…”

A Review On The Simulation of Social Networks Inside Heuristic Algorithms

“…These features offer a clear description of the population under evaluation and can be utilized for the adaptive population as well as parameter control during the metaheuristic run. The initial studies (Zelinka et al 2014;Davendra et al 2014) describing the possibilities of transforming population dynamics into complex networks were followed by the successful adaptation and control of the metaheuristic algorithm during the run through the given complex networks´ frameworks (Skanderova and Fabian 2015;Metlicka and Davendra 2015;Gajdos et al 2015;Janostik et al 2015). This research paper reviews the complex network frameworks for DE, PSO, FA and Fireworks algorithm (FWA) (Tan and Zhu 2010).…”