## 1 Introduction
In particular, the goal to explore a wide range of the state space without losing much computational time by having to laboriously cross many energetic barriers as in, e.g., MD simulations, has led to the introduction of algorithms that attempt to perform large moves on the landscape without wasting too much time in exploring uninteresting high-energy regions of the landscape. Such so-called jumpmoves are common features of many modern algorithms, and an optimal choice of the moveclass is crucial for the success of a global optimization procedure [8]; for example, one class of searches which combines every jumpmove automatically with a local deterministic (gradient-based) or stochastic (downhill random walk) minimization, and therefore moves from local minimum to local minimum, has been called the basin-hopping algorithm (BH) [29; 30]. Similarly, the genetic algorithms, which belong to the class of multi-walker algorithms that generate new states from two (or more) old states by combining their parameter values, are nowadays often combined with a local minimization of the newly generated state [31]. Nevertheless, oversampling is still a major problem. Even though the multiple discovery of the same minima is often interpreted as the heuristic criterion of the success (sometimes called \"convergence\") of the global optimization, thus turning oversampling from a bug into a feature, performing orders of magnitude more local optimizations than necessary greatly reduces the efficiency of the search algorithm. Due to the wide variety of fields where global optimizations of cost functions take place, many of these algorithms have been re-invented, or have been transferred from one area of applications to another, where one often has to adapt the original algorithm to the specific aspects of the new class of systems under consideration. An example of such a transfer with adaptation is the application of the RRT (Rapidly exploring Random Tree) algorithm [32], which was developed in the field of robotics to solve the so-called motion planning problem [33], to the field of chemistry. Indeed, solving the robot motion planning problem requires algorithms to explore the state space of the robot system aiming to find feasible trajectories between an initial and a final state. The same type of algorithms, with some adaptations, can be applied to explore the energy landscape of atomic or molecular systems [34]. Of particularly great potential to yield a qualitative and not only quantitative improvement in the development of global optimization algorithms are those methods which combine tools and (sub)-algorithms from different fields into a new type of algorithm. In this study, we present the IGLOO (Iterative Global Exploration and Local Optimization) algorithm, which combines concepts from the RRT algorithm and the threshold algorithm to efficiently explore the low-energy regions of the landscape. In the past, an earlier version of this algorithm has been successfully applied to the study of disaccharide molecules on metal surfaces, predicting the shape of the molecules on the surface and thus allowingan interpretation of the STM (Scanning Tunneling Microscopy) images obtained in the experiment [35]. In the present investigation, we analyze the performance of this algorithm in detail, using two chain-type molecules as examples. The outcomes of these global optimizations are compared to the performance of two similar types of algorithms, a classical basin-hopping algorithm [36] and a hybrid BH-RRT algorithm [37].