Abstract
This chapter exclusively addresses the algorithms employed to perform
geometry optimization of clusters. These algorithms can be broadly classified into two
groups: gradients-based algorithms and gradient-free algorithms. Gradient-based
algorithms use the gradient of potential energy functions to give local minima. On the
contrary, gradient-free algorithms are inspired by natural processes, which exploit
some mathematical models, which lead to global minimum. Although there are a
variety of gradient-free algorithms, some of the most popular ones include genetic
algorithm, particle swarm, simulated annealing, etc. The strengths and weaknesses of
all these algorithms have been also discussed.
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