Digital technologies can augment civic participation by facilitating the expression of detailed political preferences. Yet, digital participation efforts often rely on methods optimized for elections involving a few candidates. Here we present data collected in an online experiment where participants built personalized government programmes by combining policies proposed by the candidates of the 2022 French and Brazilian presidential elections. We use this data to explore aggregates complementing those used in social choice theory, finding that a metric of divisiveness, which is uncorrelated with traditional aggregation functions, can identify polarizing proposals. These metrics provide a score for the divisiveness of each proposal that can be estimated in the absence of data on the demographic characteristics of participants and that explains the issues that divide a population. These findings suggest that divisiveness metrics can be useful complements to traditional aggregation functions in direct forms of digital participation.

Swarm intelligence is a very efficient field for the optimization of high-dimensional functions. Nevertheless, choosing the best swarm-based algorithm is still challenging because it requires expertise in the field. Here, we propose to use reinforcement learning to dynamically select the swarm-based techniques to solve a benchmark function based on the current simulation state. First, we created a swarm capable of modifying its metaphor over iteration. Next, we created a reinforcement learning environment to solve benchmark functions. Then, we trained Proximal Policy Optimization to select the well-suited metaheuristic (GWO, GPSO or LPSO) to solve Rastrigin and F3 based on the information retrieved from the simulation. Our proposal reached competitive results in all simulated scenarios. Moreover, we found that the use of GPSO is consistently more efficient at the middle of the convergence and that using GWO is more efficient than using the other selected algorithms at the beginning of the convergence. Future works will bring us more robustness in combining swarm-based techniques while decreasing the computational cost. Thus, we show that reinforcement learning has the potential to overcome the effort of choosing the well-suited metaheuristic for a specific problem.

Swarm intelligence (SI) algorithms have become popular due to their self-learning characteristics and adaptability to external changes. They can find reasonable solutions to complex problems without in-depth knowledge. Much of the success of these algorithms comes from balancing the exploration and exploitation tasks. This work evaluates the application and performance of a reinforcement learning approach applied to a well-known swarm intelligence algorithm, Particle Swarm Optimization (PSO). We use the reinforcement learning agent Proximal Policy Optimization (PPO) to dynamically change the swarm communication topology according to the problem. We analyze the PSO’s behavior, influenced by the reinforcement learning agent, through methods such as interaction networks and fitness analysis. We show that the RL approach can transfer the knowledge learned from one function to other functions, and that dynamic changes of topology over time makes PSO much more efficient than setting only one specific topology, even when using a Dynamic topology. Our results then suggest that changing topologies might be more efficient than having a Dynamic topology, and that indeed Local and Global topologies have an important role in the best swarm performance. Our results take a step further on explaining the performance of SI and automatizing their use for non-experts.

O governo do estado do Rio Grande do Sul criou o programa INOVA RS com o propósito de fomentar a colaboração entre diversos atores do ecossistema de inovação gaúcho, visando impulsionar o desenvolvimento econômico e social regional. Como uma iniciativa decorrente desse programa, surgiu o projeto INOVA ID RS, que busca a construção de uma identidade digital para o ecossistema de inovação, bem como a implementação de uma federação destinada ao compartilhamento seguro de serviços digitais. Este trabalho apresenta os resultados preliminares da concepção e desenvolvimento da Federação INOVA ID RS, que está baseada nos padrões SAML e OpenId Connect.

A recent study catalogued hundreds of meta-heuristics proposed over the past three decades in Swarm Intelligence (SI) literature. This scenario makes it difficult for the practitioner to choose the most suitable meta-heuristic (RL) for a specific problem. This paper shows that Reinforcement Learning could be a powerful tool for SI. First, we describe a Reinforcement Learning environment to solve an optimization problem. Then, we investigate the usage of Proximal Policy Optimization to dynamically set the Particle Swarm Optimization topology accordingly to the simulation states. Our RL proposal reached competitive fitness values, even when evaluated in non-trained scenarios. In addition, we show the actions' distribution by simulation in the Rastrigin. The paper demonstrates how RL could be integrated to improve meta-heuristics capabilities, opening new research paths where RL will be used to improve meta-heuristics or select them accordingly to their strengths.

Swarm Intelligence is applied to optimisation problems due to its robustness, scalability, generality, and flexibility. Based on simple rules, simple reactive agents - swarm (e.g. fish, bird, and ant) - directly or indirectly exchange information to find an optimal solution. Among multiple nature inspirations and versions, the dilemma of choosing proper swarm-based algorithms for each type of problem prevents their recurrent application. This scenario gets even more challenging when considering binary optimisation because of the absence of overview papers that assembles the trends, benefits and limitations of swarm-based techniques. Based on 403 scientific papers, we describe the basis of the leading binary swarm-based algorithms presenting their rationales, equations, pseudocodes, and descriptions of their applications to tackle this research gap. We also define a new classification based on the mechanism to update the solutions and the displacements, indicating that the Binary-Binary approach - binary decision variables and binary search space - is more efficient for binary optimisation in accuracy and computational cost.

Fish School Search (FSS) is a swarm-intelligence subfamily of algorithms proposed by Bastos Filho and Lima Neto in 2008 and first published in 2009. In the FSS, the simple reactive agents are called fish, and each fish has a weight that represents the success obtained during the search. The weights' values and variations influence the individual and collective movements. The embedded mechanisms of feeding and coordinated action make the school move toward the positive gradient to gain weight (and find local and global better positions). Heavier fish have more influence in guiding the search. The idea of accumulating success along the examination indicates that a specific simple reactive agent is worth influencing others. FSS was designed for continuous optimization problems in multimodal search spaces. It has also influenced other researchers to propose variations for other issues, such as optimization in binary problems, multi-objective optimization, many-objective optimization, and multimodal optimization. In this chapter, we present a review of the advances considering FSS in the last decade, including some proposals for binary optimization, three approaches for multi- and many-objective optimizations, and two different multimodal optimization proposals. We also show two other methods for parallel processing, which aim to accelerate the processing time. We finalize the chapter giving some examples of applications of those recent approaches in real-world problems.