Design and Implementation of Differential Evolution Algorithm on FPGA for Double-Precision Floating-Point Representation

Literature Review on Differential Evolution Algorithm

“…Tanabe and Fukunaga(Tanabe & Fukunaga, 2014) developed an adaptation of DE using linear population size reduction that decreases the population size gradually using linear function. Cortés-Antonio et al(2014) presents the results of implementation of differential evolution algorithm on a field programmable gate array device (FPGA) using floating point representation with double precision.Ali Wagdy Mohamed (Ali Wagdy Mohamed, 2015) introduced a triangular mutation rule based on convex combination vector.Wu et al (Wu et al, 2016)introduced a multi-population-based structure to identify an adapted collection of threemutation strategies. The population is dynamically dividedinto severalsub-populations includingone reward sub-population and three indicator sub-populations.…”

“…Figure 4 shows the percentage of publications of the modifications of the parameters of the algorithm. Qin, Huang, and Suganthan(2008) Vector generation control parameters Das et al(2009) Vector generation Mutation functions Gong et al (Gong et al, 2010) Learning procedures Brest and Maucec(Brest & Maučec, 2011) Learning procedures Mallipeddi et al(2011) Mutation functions control parameters Vector generation Wang, Cai, and Zhang(2011) Learning procedures Vector generation control parameters L. Wang et al (2012) Learning procedures Caraffini et al(2013) Learning procedures Tanabe and Fukunaga(2013) control parameters Tanabe and Fukunaga (Tanabe & Fukunaga, 2014) Population Author Modifications Cortés-Antonio et al(2014) Learning procedures Ali Wagdy Mohamed (Ali Wagdy Mohamed, 2015) Mutation functions Wu et al (Wu et al, 2016)…”