JournalTOCs

Swarm Intelligence
Journal Prestige (SJR): 0.709

Citation Impact (citeScore): 3
Number of Followers: 3

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1935-3820 - ISSN (Online) 1935-3812
Published by Springer-Verlag

[2468 journals]

Consensus decision-making in artificial swarms via entropy-based local
negotiation and preference updating
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  Abstract: This paper presents an entropy-based consensus algorithm for a swarm of artificial agents with limited sensing, communication, and processing capabilities. Each agent is modeled as a probabilistic finite state machine with a preference for a finite number of options defined as a probability distribution. The most preferred option, called exhibited decision, determines the agent’s state. The state transition is governed by internally updating this preference based on the states of neighboring agents and their entropy-based levels of certainty. Swarm agents continuously update their preferences by exchanging the exhibited decisions and the certainty values among the locally connected neighbors, leading to consensus towards an agreed-upon decision. The presented method is evaluated for its scalability over the swarm size and the number of options and its reliability under different conditions. Adopting classical best-of-N target selection scenarios, the algorithm is compared with three existing methods, the majority rule, frequency-based method, and k-unanimity method. The evaluation results show that the entropy-based method is reliable and efficient in these consensus problems.
  PubDate: 2023-05-15
Effect of swarm density on collective tracking performance
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  Abstract: How does the size of a swarm affect its collective action' Despite being arguably a key parameter, no systematic and satisfactory guiding principles exist to select the number of units required for a given task and environment. Even when limited by practical considerations, system designers should endeavor to identify what a reasonable swarm size should be. Here, we show that this fundamental question is closely linked to that of selecting an appropriate swarm density. Our analysis of the influence of density on the collective performance of a target tracking task reveals different ‘phases’ corresponding to markedly distinct group dynamics. We identify a ‘transition’ phase, in which a complex emergent collective response arises. Interestingly, the collective dynamics within this transition phase exhibit a clear trade-off between exploratory actions and exploitative ones. We show that at any density, the exploration–exploitation balance can be adjusted to maximize the system’s performance through various means, such as by changing the level of connectivity between agents. While the density is the primary factor to be considered, it should not be the sole one to be accounted for when sizing the system. Due to the inherent finite-size effects present in physical systems, we establish that the number of constituents primarily affects system-level properties such as exploitation in the transition phase. These results illustrate that instead of learning and optimizing a swarm’s behavior for a specific set of task parameters, further work should instead concentrate on learning to be adaptive, thereby endowing the swarm with the highly desirable feature of being able to operate effectively over a wide range of circumstances.
  PubDate: 2023-03-21
  DOI: 10.1007/s11721-023-00225-4
Multi-agent bandit with agent-dependent expected rewards
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  Abstract: Many studies on the exploration policies for stochastic multi-agent bandit (MAB) problems demonstrate that integrating the experience of other group members accelerates the learning of optimal actions. However, the basic assumption of the classical MAB problem that the expected rewards are agent-independent is invalid in many real-world problems. The group members have different expected rewards for the possible actions, perhaps due to the different initial states or local environments. To solve the MAB problem with agent-dependent expected rewards, we develop a decentralized exploration policy in which agents apply confidence-weighting to integrate the experience of other group members and to estimate the expected rewards. Theoretical analysis demonstrates that the acceleration of learning still works in the agent-dependent case, and numerical simulation results verify that the proposed exploration policy outperforms the state-of-the-art method.
  PubDate: 2023-03-18
  DOI: 10.1007/s11721-023-00224-5
Cross-disciplinary approaches for designing intelligent swarms of drones
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  PubDate: 2023-02-14
  DOI: 10.1007/s11721-023-00223-6
Out-of-the-box parameter control for evolutionary and swarm-based
algorithms with distributed reinforcement learning
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  Abstract: Parameter control methods for metaheuristics with reinforcement learning put forward so far usually present the following shortcomings: (1) Their training processes are usually highly time-consuming and they are not able to benefit from parallel or distributed platforms; (2) they are usually sensitive to their hyperparameters, which means that the quality of the final results is heavily dependent on their values; (3) and limited benchmarks have been used to assess their generality. This paper addresses these issues by proposing a methodology for training out-of-the-box parameter control policies for mono-objective non-niching evolutionary and swarm-based algorithms using distributed reinforcement learning with population-based training. The proposed methodology is suitable to be used in any mono-objective optimization problem and for any mono-objective and non-niching Evolutionary and swarm-based algorithm. The results in this paper achieved through extensive experiments show that the proposed method satisfactorily improves all the aforementioned issues, overcoming constant, random and human-designed policies in several different scenarios.
  PubDate: 2023-01-07
  DOI: 10.1007/s11721-022-00222-z
Three-dimensional relative localization and synchronized movement with
wireless ranging
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  Abstract: Relative localization is a key capability for autonomous robot swarms, and it is a substantial challenge, especially for small flying robots, as they are extremely restricted in terms of sensors and processing while other robots may be located anywhere around them in three-dimensional space. In this article, we generalize wireless ranging-based relative localization to three dimensions. In particular, we show that robots can localize others in three dimensions by ranging to each other and only exchanging body velocities and yaw rates. We perform a nonlinear observability analysis, investigating the observability of relative locations for different cases. Furthermore, we show both in simulation and with real-world experiments that the proposed method can be used for successfully achieving various swarm behaviours. In order to demonstrate the method’s generality, we demonstrate it both on tiny quadrotors and lightweight flapping wing robots.
  PubDate: 2022-12-02
  DOI: 10.1007/s11721-022-00221-0
Phase transition of a nonlinear opinion dynamics with noisy interactions
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  Abstract: In several real Multi-Agent Systems, it has been observed that only weaker forms of metastable consensus are achieved, in which a large majority of agents agree on some opinion while other opinions continue to be supported by a (small) minority of agents. In this work, we take a step towards the investigation of metastable consensus for complex (nonlinear) opinion dynamics by considering the popular Undecided-State dynamics in the binary setting, which is known to reach consensus exponentially faster than the Voter dynamics. We propose a simple form of uniform noise in which each message can change to another one with probability p and we prove that the persistence of a metastable consensus undergoes a phase transition for \(p=\frac{1}{6}\) . In detail, below this threshold, we prove the system reaches with high probability a metastable regime where a large majority of agents keeps supporting the same opinion for polynomial time. Moreover, this opinion turns out to be the initial majority opinion, whenever the initial bias is slightly larger than its standard deviation. On the contrary, above the threshold, we show that the information about the initial majority opinion is “lost” within logarithmic time even when the initial bias is maximum. Interestingly, we show our results have explicit connections to two different concrete frameworks. The first one concerns a specific setting of a well-studied value-sensitive decision mechanism inspired by cross-inhibition in house-hunting honeybee swarms. The second framework consists of a consensus process where a subset of agents behave in a stubborn way.
  PubDate: 2022-11-17
  DOI: 10.1007/s11721-022-00217-w
Blending multiple algorithmic granular components: a recipe for clustering
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  Abstract: Emerging trends in algorithm design have shown that hybrid algorithms, which combine or merge multiple algorithms, can create synergies to overcome the inherent limitations of the underlying individual algorithms. There are two broad types of hybridization: collaborative—individual algorithms tackle an instance of the problem sequentially or in parallel and exchange information accordingly while solving the problem; integrative—individual algorithms are dedicated to tackling different aspect(s) of the problem-solving process. In this research, we propose a schema for an enhanced form of integrative hybridization that blends granular algorithmic components from multiple algorithms to derive a new singular clustering algorithm. As a case study, we examine the ant clustering algorithm (a swarm intelligence algorithm that is based on the natural phenomenon of brood sorting in some species of ants); highlight the strengths and weaknesses of the algorithm; and present a blend of algorithmic components from Tabu search into the algorithm to improve its exploration strategy and solution quality. Empirical results from applying the blended algorithm to clustering benchmark datasets show improved clustering validation measures for the proposed blended hybrid algorithm compared to other hybridization of the same underlying individual algorithms. Besides, the quality of clusters uncovered by this hybrid algorithm competes favorably with those uncovered using popular clustering algorithms such as DBSCAN and mean shift.
  PubDate: 2022-11-06
  DOI: 10.1007/s11721-022-00219-8
Wildfire detection in large-scale environments using force-based control
for swarms of UAVs
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  Abstract: Wildfires affect countries worldwide as global warming increases the probability of their appearance. Monitoring vast areas of forests can be challenging due to the lack of resources and information. Additionally, early detection of wildfires can be beneficial for their mitigation. To this end, we explore in simulation the use of swarms of uncrewed aerial vehicles (UAVs) with long autonomy that can cover large areas the size of California to detect early stage wildfires. Four decentralised control algorithms are tested: (1) random walking, (2) dispersion, (3) pheromone avoidance and (4) dynamic space partition. The first three adaptations are known from literature, whereas the last one is newly developed. The algorithms are tested with swarms of different sizes to test the spatial coverage of the system in 24 h of simulation time. Best results are achieved using a version of the dynamic space partition algorithm (DSP) which can detect 82% of the fires using only 20 UAVs. When the swarm consists of 40 or more aircraft 100% coverage can also be achieved. Further tests of DSP show robustness when agents fail and when new fires are generated in the area.
  PubDate: 2022-11-01
  DOI: 10.1007/s11721-022-00218-9
Collective gradient perception with a flying robot swarm
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  Abstract: In this paper, we study the problem of collective and emergent sensing with a flying robot swarm in which social interactions among individuals lead to following the gradient of a scalar field in the environment without the need of any gradient sensing capability. We proposed two methods—desired distance modulation and speed modulation—with and without alignment control. In the former, individuals modulate their desired distance to their neighbors and in the latter, they modulate their speed depending on the social interactions with their neighbors and measurements from the environment. Methods are systematically tested using two metrics with different scalar field models, swarm sizes and swarm densities. Experiments are conducted using: (1) a kinematic simulator, (2) a physics-based simulator, and (3) real nano-drone swarm. Results show that using the proposed methods, a swarm—composed of individuals lacking gradient sensing ability—is able to follow the gradient in a scalar field successfully. Results show that when individuals modulate their desired distances, alignment control is not needed but it still increases the performance. However, when individuals modulate their speed, alignment control is needed for collective motion. Real nano-drone experiments reveal that the proposed methods are applicable in real-life scenarios.
  PubDate: 2022-10-26
  DOI: 10.1007/s11721-022-00220-1
Drone flocking optimization using NSGA-II and principal component analysis
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  Abstract: Individual agents in natural systems like flocks of birds or schools of fish display a remarkable ability to coordinate and communicate in local groups and execute a variety of tasks efficiently. Emulating such natural systems into drone swarms to solve problems in defense, agriculture, industrial automation, and humanitarian relief is an emerging technology. However, flocking of aerial robots while maintaining multiple objectives, like collision avoidance, high speed etc., is still a challenge. This paper proposes optimized flocking of drones in a confined environment with multiple conflicting objectives. The considered objectives are collision avoidance (with each other and the wall), speed, correlation, and communication (connected and disconnected agents). Principal Component Analysis (PCA) is applied for dimensionality reduction and understanding of the collective dynamics of the swarm. The control model is characterized by 12 parameters which are then optimized using a multi-objective solver (NSGA-II). The obtained results are reported and compared with that of the CMA-ES algorithm. The study is particularly useful as the proposed optimizer outputs a Pareto Front representing different types of swarms that can be applied to different scenarios in the real world.
  PubDate: 2022-10-26
  DOI: 10.1007/s11721-022-00216-x
A field-based computing approach to sensing-driven clustering in robot
swarms
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  Abstract: Swarm intelligence leverages collective behaviours emerging from interaction and activity of several “simple” agents to solve problems in various environments. One problem of interest in large swarms featuring a variety of sub-goals is swarm clustering, where the individuals of a swarm are assigned or choose to belong to zero or more groups, also called clusters. In this work, we address the sensing-based swarm clustering problem, where clusters are defined based on both the values sensed from the environment and the spatial distribution of the values and the agents. Moreover, we address it in a setting characterised by decentralisation of computation and interaction, and dynamicity of values and mobility of agents. For the solution, we propose to use the field-based computing paradigm, where computation and interaction are expressed in terms of a functional manipulation of fields, distributed and evolving data structures mapping each individual of the system to values over time. We devise a solution to sensing-based swarm clustering leveraging multiple concurrent field computations with limited domain and evaluate the approach experimentally by means of simulations, showing that the programmed swarms form clusters that well reflect the underlying environmental phenomena dynamics.
  PubDate: 2022-09-19
  DOI: 10.1007/s11721-022-00215-y
Noise-resistant and scalable collective preference learning via ranked
voting in swarm robotics
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  Abstract: Swarm robotics studies how to use large groups of cooperating robots to perform designated tasks. Given the need for scalability, individual members of the swarm usually have only limited sensory capabilities, which can be unreliable in noisy situations. One way to address this shortcoming is via collective decision-making, and utilizing peer-to-peer local interactions to enhance the behavioral performances of the whole swarm of intelligent agents. In this paper, we address a collective preference learning scenario, where agents seek to rank a series of given sites according to a preference order. We have proposed and tested a novel ranked voting-based strategy to perform the designated task. We use two variants of a belief fusion-based strategy as benchmarks. We compare the considered algorithms in terms of accuracy and precision of decisions as well as the convergence time. We have tested the considered algorithms in various noise levels, evidence rates, and swarm sizes. We have concluded that the proposed ranked voting approach is significantly cheaper and more accurate, at the cost of less precision and longer convergence time. It is especially advantageous compared to the benchmark when facing high noise or large swarm size.
  PubDate: 2022-09-05
  DOI: 10.1007/s11721-022-00214-z
Sample greedy based task allocation for multiple robot systems
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  Abstract: This paper addresses in-schedule dependent task allocation problems for multi-robot systems. One of the main issues with those problems is the inherent NP-hardness of combinatorial optimisation. To handle this issue, this paper develops a decentralised task allocation algorithm by leveraging the submodularity concept and a sampling process of task sets. Our theoretical analysis reveals that the proposed algorithm can provide an approximation guarantee of 1/2 of the optimal solution for the monotone submodular case and 1/4 for the non-monotone submodular case, both with polynomial time complexity. To examine the performance of the proposed algorithm and validate the theoretical analysis, we introduce two task allocation scenarios and perform numerical simulations. The simulation results confirm that the proposed algorithm achieves a solution quality which is comparable to state-of-the-art algorithms in the monotone case and much better quality in the non-monotone case with significantly lower computational complexity.
  PubDate: 2022-08-13
  DOI: 10.1007/s11721-022-00213-0
Emergent naming conventions in a foraging robot swarm
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  Abstract: In this study, we investigate the emergence of naming conventions within a swarm of robots that collectively forage, that is, collect resources from multiple sources in the environment. While foraging, the swarm explores the environment and makes a collective decision on how to exploit the available resources, either by selecting a single source or concurrently exploiting more than one. At the same time, the robots locally exchange messages in order to agree on how to name each source. Here, we study the correlation between the task-induced interaction network and the emergent naming conventions. In particular, our goal is to determine whether the dynamics of the interaction network are sufficient to determine an emergent vocabulary that is potentially useful to the robot swarm. To be useful, linguistic conventions need to be compact and meaningful, that is, to be the minimal description of the relevant features of the environment and of the made collective decision. We show that, in order to obtain a useful vocabulary, the task-dependent interaction network alone is not sufficient, but it must be combined with a correlation between language and foraging dynamics. On the basis of these results, we propose a decentralised algorithm for collective categorisation which enables the swarm to achieve a useful—compact and meaningful—naming of all the available sources. Understanding how useful linguistic conventions emerge contributes to the design of robot swarms with potentially improved autonomy, flexibility, and self-awareness.
  PubDate: 2022-07-13
  DOI: 10.1007/s11721-022-00212-1
On multi-human multi-robot remote interaction: a study of transparency,
inter-human communication, and information loss in remote interaction
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  Abstract: In this paper, we investigate how to design an effective interface for remote multi-human–multi-robot interaction. While significant research exists on interfaces for individual human operators, little research exists for the multi-human case. Yet, this is a critical problem to solve to make complex, large-scale missions achievable in which direct operator involvement is impossible or undesirable, and robot swarms act as a semi-autonomous agents. This paper’s contribution is twofold. The first contribution is an exploration of the design space of computer-based interfaces for multi-human multi-robot operations. In particular, we focus on agent transparency and on the factors that affect inter-human communication in ideal conditions, i.e., without communication issues. Our second contribution concerns the same problem, but considering increasing degrees of information loss, defined as intermittent reception of data with noticeable gaps between individual receipts. We derived a set of design recommendations based on two user studies involving 48 participants.
  PubDate: 2022-03-11
  DOI: 10.1007/s11721-021-00209-2
Distributed deformable configuration control for multi-robot systems with
low-cost platforms
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  Abstract: This work presents a deformable configuration controller—a fully distributed algorithm that enables a swarm of robots to avoid an obstacle while maintaining network connectivity. We assume a group of robots flocking in an unknown environment, each of which has only incomplete knowledge of the geometry without a map, a shared coordinate, or the use of a centralized control scheme. Instead, the controller requires only local information about the area around individual robots. We devise a phase transition machine, which designs overall obstacle avoidance procedures in a fully distributed way. Robots in collision with an obstacle distributively measure the topology of the sensor network formed by the robots in order to estimate the shape of the obstacle, and choose a motion model, either obstacle-detouring or bouncing-off, each of which deforms the network to avoid an obstacle without knowledge of the geometry around the obstacle. The robots then sense the maximum tree angle, which detects the straightness of a configuration to ensure the completion of the obstacle avoidance procedure, and perform flocking with a modified heading consensus to reconstruct a volumed network with their original headings. We provide theoretical performance analyses of the controller. We also validate the theoretical results by multiple simulations with a swarm with various population sizes.
  PubDate: 2022-02-26
  DOI: 10.1007/s11721-022-00211-2
Multi-guide particle swarm optimisation archive management strategies for
dynamic optimisation problems
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  Abstract: This study presents archive management approaches for dynamic multi-objective optimisation problems (DMOPs) using the multi-guide particle swarm optimisation (MGPSO) algorithm by Scheepers et al. (Swarm Intell, 13(3–4):245–276, 2019, https://doi.org/10.1007/s11721-019-00171-0). The MGPSO is a multi-swarm approach developed for static multi-objective optimisation problems, where each subswarm optimises one of the objectives. It uses a bounded archive that is based on a crowding distance archive implementation. This paper adapts the MGPSO algorithm to solve DMOPs by proposing alternative archive update strategies to allow efficient tracking of the changing Pareto-optimal front. To evaluate the adapted MGPSO for DMOPs, a total of twenty-nine benchmark functions and six performance measures were implemented. The problem set consists of problems with only two or three objectives, and the exact time of the changes is assumed to be known beforehand. The experiments were run against five different environment types, where both the frequency of changes and the severity of changes parameters control how often and how severe the changes are during the optimisation of a DMOP. The best archive management approach was compared to the other state-of-the-art dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that MGPSO with a local search approach to the archive management achieves very competitive and oftentimes better performance when compared with the other DMOAs.
  PubDate: 2022-02-01
  DOI: 10.1007/s11721-022-00210-3
Metaphor-based metaheuristics, a call for action: the elephant in the room
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  PubDate: 2021-11-30
  DOI: 10.1007/s11721-021-00202-9
Causes of variation of darkness in flocks of starlings, a computational
model
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  Abstract: The coordinated motion of large flocks of starlings is fascinating for both laymen and scientists. During their aerial displays, the darkness of flocks often changes, for instance dark bands propagate through the flock (so-called agitation waves) and small or large parts of the flock darken. The causes of dark bands in agitation waves have recently been shown to depend on changes in orientation of birds relative to the observer rather than changes in density of the flock, but what causes other changes in darkness need to be studied still and this is the aim of the present investigation. Because we cannot empirically relate changes in darkness in flocks to quantities, such as position and orientation of the flock and of its members relative to the observer, we study this in a computational model. We use StarDisplay, a model of collective motion of starlings, because its flocks resemble empirical data in many properties, such as their three-dimensional shape, their manner of turning, the correlation of heading of its group-members, and its internal structure regarding density and stability of neighbors. We show that the change in darkness in the flocks perceived by an observer on the ground mostly depends on the observer’s distance to the flock and on the degree of exposure of the wing surface of flock members to the observer, and that darkness appears to decrease when birds roll during sharp turns. Remarkably, the darkness of the flock perceived by the observer was neither affected by the orientation of the flock relative to the observer nor by the density of the flock. Further studies are needed to investigate changes in darkness for flocks under predation.
  PubDate: 2021-11-25
  DOI: 10.1007/s11721-021-00207-4