This article by Zelda B. Zabinsky provides an overview of random search algorithms, which are particularly useful for tackling complex global optimization problems with either continuous or discrete variables. These algorithms, including simulated annealing, genetic algorithms, and particle swarm optimization, leverage randomness or probability in their iterative processes, often falling under the category of metaheuristics. Such methods are valuable for problems characterized by nonconvex, nondifferentiable, or discontinuous objective functions, as they offer a trade-off between optimality and computational speed. Random search algorithms can be categorized by their approach to exploration versus exploitation, and their application spans various fields, including engineering, scheduling, and biological systems. They address challenges where traditional deterministic methods struggle, particularly in the absence of clear structures distinguishing local from global optima.
This discussion explores the effectiveness of simulated annealing compared to random search for optimizing a set of 16 integer parameters. The author seeks to determine if simulated annealing provides a significant advantage over random search, despite the parameter space being too large for exhaustive search. Responses suggest plotting performance over time and highlight the ability of simulated annealing to escape local optima as its main strength.
