In certain real-world optimization scenarios, practitioners are not interested in solving multiple problems but rather in finding the best solution to a single, specific problem. When the computational budget is large relative to the cost of evaluating a candidate solution, multiple heuristic alternatives can be tried to solve the same given problem, each possibly with a different algorithm, parameter configuration, initialization, or stopping criterion. The sequential selection of which alternative to try next is crucial for efficiently identifying the one that provides the best possible solution across multiple attempts. Despite the relevance of this problem in practice, it has not yet been the exclusive focus of any existing review. Several sequential alternative selection strategies have been proposed in different research topics, but they have not been comprehensively and systematically unified under a common perspective. This work presents a focused review of single-problem multi-attempt heuristic optimization. It brings together suitable strategies to this problem that have been studied separately through algorithm selection, parameter tuning, multi-start and resource allocation. These strategies are explained using a unified terminology within a common framework, which supports the development of a taxonomy for systematically organizing and classifying them.

Air power has played an increasingly important role in the Libyan conflict. The relatively flat featureless desert terrain of the north and coast means that ground units are easily spotted, with few places to hide. The air forces of both the United Nations-recognised Government of National Accord (GNA) and eastern-based commander Khalifa Haftar and his self-styled Libyan National Army (LNA) use French and Soviet-era fighter jets, antiquated and poorly maintained. While manned fighter aircraft have been used, for the most part the air war has been fought by unmanned aerial vehicles (UAVs) or drones. With nearly 1,000 air strikes conducted by UAVs, UN Special Representative to Libya Ghassan Salame called the conflict "the largest drone war in the world".

In images taken from a satellite floating 400 kilometers above the Earth, Europe's humanitarian crisis shows up as white pixels against the blue-green vastness of the Mediterranean. Captured by the sensors in space, small overcrowded boats with migrants leaving Africa headed north look like tiny white comets bursting through the ocean, leaving a tail where they stir waves. "It's not that with every image I look at, I think about how someone could be dying right now," said Elisabeth Wittmann as she clicked through satellite footage on her laptop showing the coast west of the Libyan port of Sabratha. "That's also to protect myself," she added. The 26-year-old computer scientist from southern Germany is one of a dozen researchers who have teamed up with a new NGO called Space-Eye to develop artificial intelligence technology that allows computers to detect migrant boats in satellite images.