Comparing the Uni-Modal Scaling Performance of Global and Local Selection in a Mutation-Only Differential Evolution Algorithm

This paper benchmarks the scaling performance of two mutation-only Differential Evolution algorithms with the goal of discovering why large population sizes are often needed to support optimizations with small scale factors. The algorithms differ only in how the base vector is selected. We determined the computational efficiency of both base vector selection methods with a test bed consisting of three convex and one non-convex uni-modal functions. Our experiments revealed the best control parameter combinations and their dependence on the objective function´s dimension. In addition, results demonstrate the extent to which stretching, rotating and making the objective function landscape non-convex impact the performance of the differential mutation operator.