Seed Throwing Optimization: A Probabilistic Technique for Multimodal Function Optimization

A method for optimization of continuous nonlinear functions is introduced. Seed Throwing Optimization is a probabilistic metaheuristic. It has roots in hill climbing and the evolutionary computation like technique harmony search. The relationship to these algorithms is shown in this paper. Our method is tested in a benchmark and compared to other metaheuristics. Seed Throwing Optimization is a randomized gradient ascent with multi initial states and the possibility to explore only paths which have shown to be good. We also developed an efficient method for implementing gradient ascent without using a gradient.