Complex network analysis of differential evolution algorithm applied to flowshop with no-wait problem

Study on the Time Development of Complex Network for Metaheuristic

“…Within each iteration and for particular individual (solution), three random numbers r 1 , r 2 , r 3 are selected, unique to each other and to the current indexed solution i in the population. Two solutions, x j , r1 , G and x j , r2,G are selected through the index r 1 and r 2 and their values subtracted. This value is then multiplied by F, the predefined scaling factor.…”

“…A complex network contains features, which are unique to the assigned problem. It exhibits features such as degree distribution, clustering, and community structures etc., which are important markers for population used in Evolutionary algorithms [2]. Recently it was experimentally shown that a population under EA's exhibits such complex network behavior [1].…”

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

“…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.…”

“…In the StarCraft: Brood War [144] has used SOMA to control dynamics of combat units with better performance than computer driven units. EAs (including swarm algorithms) can also be improved itself, as reported in [47][48][49][145][146][147]. In these papers is shown how can be converted EAs dynamics into complex network then apply tools of complex networks analysis on complex networks that are given by evolutionary dynamics.…”