Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Experimental comparison of six population-based algorithms for continuous black box optimization
Evolutionary Computation
Energy landscapes of atomic clusters as black box optimization benchmarks
Evolutionary Computation
Restarted local search algorithms for continuous black box optimization
Evolutionary Computation
The continuous differential ant-stigmergy algorithm for numerical optimization
Computational Optimization and Applications
Generalized dynamical fuzzy model for identification and prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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We benchmark the Nelder-Mead downhill simplex method on the noisefree BBOB-2009 testbed. A multistart strategy is applied on two levels. On a local level, at least ten restarts are conducted with a small number of iterations and reshaped simplex. On the global level independent restarts are launched until $10^5 D$ function evaluations are exceeded, for dimension $D\ge20$ ten times less. For low search space dimensions the algorithm shows very good results on many functions. It solves 24, 18, 11 and 7 of 24 functions in 2, 5, 10 and 40-D.