Particle swarm optimization for searching efficient experimental designs: A review

The class of nature-inspired metaheuristic algorithms is increasingly used to tackle all kinds of optimization problems across disciplines. It also plays an important component in artificial intelligence and machine learning. Members in this class are general purpose optimization tools that virtually require no assumptions for them to be applicable. There are many such algorithms, and to fix ideas, we review one of its exemplary members called particle swarm optimization (PSO). We discuss the algorithm, its recent applications to find different types of efficient experimental designs, and provide resources, where codes for PSO and other metaheuristic algorithms and tutorials with examples are available. This article is categorized under: Algorithms and Computational Methods > Genetic Algorithms and Evolutionary Computing.