Adaptive Differential Evolution Algorithm for Single and Multi-Objective Numerical Optimization

“DE/current-to-pbest” is a new and increasingly common mutation strategy that involves an additional external archive and adaptively updates the control. This thesis introduces a novel algorithm known as JADE. The “DE/current-to-pbest” is a simplification of the typical “DE/current-to-best,” while historical data is used by the additional archive operation to provide information on progress direction. Both convergence performance and the diversity of the population are enhanced by the two operations. The control parameters are automatically updated to the appropriate values through parameter adaptation, which avoids relying on outdated information regarding the relationship between the characteristics of the optimization problems and the parameter settings. This thesis work introduces a JADE Algorithm and examines its feasibility based on the results of CEC'17 expensive benchmark problems for single objective optimization problems and for Multi-objective optimization. The methods used in our studies are compared to different well-knows methods proposed in the related literature was conducted. The final ranking of all test problems indicate that JADE was always among the top best algorithms that were used for the same purpose.