Particle swarm optimization: A survey of innovations over the last 10 years

The Particle Swarm Optimization (PSO) is a well-known metaheuristic algorithm whose impact on optimization is invaluable. Its simple design, proposed in 1995, has been the inspiration for several related approaches, some of which are pillars in the literature. Additionally, the PSO has parameters that can be tuned from different perspectives, which has led to its application in numerous areas and to its hybridization with most of the evolutionary methods in the state-of-the-art. These features have highlighted the need to present ongoing review work that offers an overview of PSO’s contributions to the identified research areas. Nonetheless, the algorithm’s relevance has made it increasingly difficult to summarize its overall impact in a single article. Furthermore, many related works that limit their scope tend to categorize the PSO literature into mixed categories, which may confuse readers rather than guide them toward a deeper understanding of the approach. Therefore, this article presents an exhaustive survey of the PSO, highlighting three remarkable aspects. First, the taxonomy is limited to the last decade to illustrate the scheme’s impact in the contemporary world. Secondly, a structure of seven main categories is used to define the areas where the optimizer has contributed. Finally, different statistical analyses enable the reader to understand the evolution of the method and its future directions, which is particularly important for new PSO research. One of the principal purposes of this survey is to serve as an expanded summary of the latest advances in the PSO algorithm over a 10-year period. This study is expected to generate interest among researchers in the field, providing a comprehensive overview of the PSO algorithm’s performance and serving as a valuable resource for experienced researchers.