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The purpose of this paper is to present an approach for the decentralization of swarm intelligence algorithms that run on computing systems with autonomous components that are connected by a network. The approach is applied to a particle swarm optimization (PSO) algorithm with multiple sub‐swarms. PSO is a nature inspired metaheuristic where a swarm of particles searches for an optimum of a function. A multiple sub‐swarms PSO can be used for example in applications where more than one optimum has to be found.

In the studied scenario the particles of the PSO algorithm correspond to data packets that are sent through the network of the computing system. Each data packet contains among other information the position of the corresponding particle in the search space and its sub‐swarm number. In the proposed decentralized PSO algorithm the application specific tasks, i.e. the function evaluations, are done by the autonomous components of the system. The more general tasks, like the dynamic clustering of data packets, are done by the routers of the network.

Simulation experiments show that the decentralized PSO algorithm can successfully find a set of minimum values for the used test functions. It was also shown that the PSO algorithm works well for different type of networks, like scale‐free network and ring like networks.

The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self‐organization and have no central control.

Supreme audit institutions (SAIs) examine and supervise the activity of public institutions. The study aims at contributing to the existing literature on public sector audit by providing a classification of 29 European SAIs – 28 national SAIs and the European Court of Auditors (ECA) – based on a broad range of attributes varying from the SAIs' environment to its structure, activity, resources or transparency.

The authors apply quantitative methodology for clustering by means of multidimensional scaling and regressive ordinary least square (OLS) and logistic models.

The authors' results show that SAIs from veteran EU member states (MSs) are more similar amongst them and the same applies to SAIs from Nordic countries, Baltic countries, Western Mediterranean countries and Eastern countries. The authors also perform additional analysis focussing on currently relevant issues such as gender equality, age, environment or the sustainable development goals (SDGs), concluding that the younger the institutions' staff, the more transparent the institutions are. The authors also find that more transparent SAIs report on environmental audits, more prone to cover the SDGs in their audits.

The research is limited in purpose and scope because data cover only Europe. Given the limited number of observations (29), it does not have prospective purposes but only explanatory ones. The authors' findings are interesting for researchers because they offer original insights on public audit in Europe and cover matters of current interest such as environment, transparency or gender equality.

The research is also of interest for public auditors because it offers them information that may help them improve their activity and find institutional synergies, as the dataset is available to public auditors.

From a social view point, the paper shows that public auditors perform work on topics of interest for the citizens.

The dataset compiled for the research offers extensive data and a wide variety of attributes defining European SAIs and may offer future opportunities for research from different perspectives.