Random Search Algorithms
Zelda B. Zabinsky · Wiley Encyclopedia of Operations Research and Management Science · 2010
Abstract Random search algorithms are useful for many ill‐structured global optimization problems with continuous and/or discrete variables. Typically random search algorithms sacrifice a guarantee of optimality for finding a good solution quickly with convergence results in probability. Random search algorithms include simulated annealing, tabu search, genetic algorithms, evolutionary programming, particle swarm optimization, ant colony optimization, cross‐entropy, stochastic approximation, multistart and clustering algorithms, to name a few. They may be categorized as global (exploration) versus local (exploitation) search, or instance‐based versus model‐based. However, one feature these methods share is the use of a random element embedded in their iterative procedures. This article provides an overview of random search algorithms used to solve black‐box global optimization problems.