# Differential Evolution: A Survey and Analysis
## 4 Conclusions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Strategy** & **Note** \\ \hline Multi Population DE algorithm (MPDE) [116] & DE/best/1 & \begin{tabular}{l} MPDE created subpopulation in random \\ manner from the main population and then \\ the migration of the best vector from \\ subpopulations to main population \\ \end{tabular} \\ \hline Adaptive DE [117] & Six DE strategies and one strategy is randomly selected by a roulettewheel & Adaptively selects trial vector generation, scale factor “F” \(\pm\) 0.8 and is constant for all strategies, also the crossover rate is constant as well that is 0.5 \\ \hline Self-Adapting Parameter Setting in Differential Evolution (jDE) & DE/rand/1/bin & \begin{tabular}{l} jDE enhances the population size through \\ the optimization procedure based on the \\ developed DE parameters \\ \end{tabular} \\ \hline Self-adaptive Mutation DE (SaMDE) & DE/rand/1, DE/best/1, DE/best/2 andDE/current-to-rand/1 & The strategy is chosen by a roulette wheel \\ \hline Modified DE(MDE)with pbest crossover(MDE-p8Q) [66] & DE/current-to-best/1, DE/current-to-g\_best/1 & \begin{tabular}{l} F and \(C\) directed by the information of \\ their effective values that are capable of \\ producing improved offspring \\ \end{tabular} \\ \hline Modified DE algorithm (MDE) & DE/rand/1, DE/best/1 & One of two strategies is chosen based on a \\ \hline DE with Self-Adaptive Mutation and Crossover (DESAMC) & Classic DE Strategy & Working to self-adapt the parameters \\ \hline Differential Covariance Matrix Adaptation & New population vector is created using & Parameters are chosen randomly \\ Evolutionary Algorithm (CMA-ES)\({}^{\ast}\) & DE/rand/1/bin & \begin{tabular}{l} Parameters are chosen randomly \\ \end{tabular} \\ \hline Differential Evolution with Multiple Strategies & \
\begin{tabular}{l} DE/best/1/bin,rand/1/bin, DE/best/1/exp, \\ and DE/rand/1/exp \\ \end{tabular} & Parameters are chosen randomly \\ \hline DE-PSO & Classic DE strategy + The two basic equations which govern the working of PSO &
\begin{tabular}{l} “DE-PSO” Hybrid differential evolution - \\ Particle Swarm Optimization. The inclusion \\ of PSO phase creates a perturbation in the \\ population, which in turn helps in \\ maintaining diversity of the population and \\ producing a good optimal solution. \\ \end{tabular} \\ \hline Hybrid of DE and GA (hGADE) [118] & Hybridized GA with only 2 classical DE variants & Randomly generated binary unit \\  & Hybridized GA with only 2 classical DE variants & commitment matrices while the RPM of all \\  & the individuals in the initial population are generated \\ \hline Hybridization of DE and Biogeography Based & Classical DE DE/rand/1/bin + classic BBO & The main operator of DE/BBO is the hybrid \\ Optimization (BBO) [119] & Classical DE DE/rand/1/bin + classic BBO & migration operator, \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of different DE algorithms with variety of approaches. **Author Contributions**: Conceptualization of this paper survey which is carried to gain extensive ideas about DE and verifies of techniques [PERSON] created the structure and organization of the paper, reviewed and instituted the content in all sections and commented on the quantitative aspects of the DE algorithm. [PERSON] co-reviewed the paper. [PERSON] contributed to the final version of the manuscript. All authors approve of the final version of the manuscript. **Acknowledgments**: The authors acknowledged the University of Bridgeport for the preparation of resources which are required to complete this research. The University of Bridgeport library contributes by arranging required books and researched papers in this work. The University of Bridgeport, CT USA also pledge us by funding the cost of publishing this paper. **Conflicts of Interest**: The authors declare no conflict of interest.