Using a well known benchmarking and profiling environment, we compare the performances of three simple and easy to use metaheuristics for global optimization: Differential Evolution, Basin Hopping and Particle Swarm Optimization. The comparison was done on a test set of 24 functions featuring many characteristics found on real-world problems and on four different space dimensions. Our results statistically show that there is no clear winner overall. The three methods perform well in general and the actual differences are related to the different groups of functions in the benchmark with Basin Hopping being the most robust technique, and Differential Evolution and Particle Swarm Optimization excelling on highly multi-modal functions.
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