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  • Spatial segregative behaviors in robotic swarms using differential potentials
    Swarm Intell. (IF 2.556) Pub Date : 2020-07-02
    Vinicius G. Santos, Anderson G. Pires, Reza J. Alitappeh, Paulo A. F. Rezeck, Luciano C. A. Pimenta, Douglas G. Macharet, Luiz Chaimowicz

    Segregative behaviors, in which individuals with common characteristics are placed together and set apart from other groups, are commonly found in nature. In swarm robotics, these behaviors can be important in different tasks that require a heterogeneous group of robots to be divided in homogeneous sets according to their physical (sensors, actuators) or logical (algorithms) capabilities. In this paper

    更新日期:2020-07-03
  • Response probability enhances robustness in decentralized threshold-based robotic swarms
    Swarm Intell. (IF 2.556) Pub Date : 2020-06-06
    Annie S. Wu, R. Paul Wiegand, Ramya Pradhan

    In this paper, we investigate how response probability may be used to improve the robustness of reactive, threshold-based robotic swarms. In swarms where agents have differing thresholds, adding a response probability is expected to distribute task experiences among more agents, which can increase the robustness of the swarm. If the lowest threshold agents for a task become unavailable, distributing

    更新日期:2020-06-06
  • On the robustness of consensus-based behaviors for robot swarms
    Swarm Intell. (IF 2.556) Pub Date : 2020-05-29
    Majda Moussa, Giovanni Beltrame

    In swarm robotics, behaviors requiring consensus, meaning having the robots agree on a set of variables, have attracted great attention over the years. Determining the robustness and applicability of these behaviors in harsh communication environments is an open area of research. In this paper, we propose the use of a formal software engineering technique, statistical model checking, to model and assess

    更新日期:2020-05-29
  • A simplified multi-objective particle swarm optimization algorithm
    Swarm Intell. (IF 2.556) Pub Date : 2019-07-15
    Vibhu Trivedi, Pushkar Varshney, Manojkumar Ramteke

    Particle swarm optimization is a popular nature-inspired metaheuristic algorithm and has been used extensively to solve single- and multi-objective optimization problems over the last two decades. Several local and global search strategies, and learning and parameter adaptation strategies have been included in particle swarm optimization to improve its performance over the years. Most of these approaches

    更新日期:2020-04-22
  • A computational model of task allocation in social insects: ecology and interactions alone can drive specialisation
    Swarm Intell. (IF 2.556) Pub Date : 2020-02-22
    Rui Chen, Bernd Meyer, Julian Garcia

    Social insects allocate their workforce in a decentralised fashion, addressing multiple tasks and responding effectively to environmental changes. This process is fundamental to their ecological success, but the mechanisms behind it are not well understood. While most models focus on internal and individual factors, empirical evidence highlights the importance of ecology and social interactions. To

    更新日期:2020-04-22
  • An analytical study on leader and follower switching in V-shaped Canada Goose flocks for energy management purposes
    Swarm Intell. (IF 2.556) Pub Date : 2020-01-30
    A. Mirzaeinia, F. Heppner, M. Hassanalian

    Migrating birds may take advantage of V-shaped flocking to reduce the required energy for their flight. Studies have shown that the birds in different positions in V-shaped flight contend with different drag forces. Lead and follower birds may have to overcome more drag forces than the other birds in a V-shaped flock. Some observations of different kinds of flocking birds repositioning within a flock

    更新日期:2020-04-22
  • Respecializing swarms by forgetting reinforced thresholds
    Swarm Intell. (IF 2.556) Pub Date : 2020-03-05
    Vera A. Kazakova, Annie S. Wu, Gita R. Sukthankar

    Response threshold reinforcement is a powerful model for decentralized task allocation and specialization in multiagent swarms. In dynamic environments, initial task assignments and specializations must be updated over time to meet changing system needs. The very nature of threshold reinforcement-based behavior can, however, hinder respecialization, limiting its usability in real-world applications

    更新日期:2020-04-22
  • Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms
    Swarm Intell. (IF 2.556) Pub Date : 2019-10-03
    Antoine Ligot, Mauro Birattari

    The reality gap—the discrepancy between reality and simulation—is a critical issue in the off-line automatic design of control software for robot swarms, as well as for single robots. It is understood that the reality gap manifests itself as a drop in performance: when control software generated in simulation is ported to physical robots, the performance observed is often disappointing compared with

    更新日期:2020-04-22
  • A study on force-based collaboration in swarms
    Swarm Intell. (IF 2.556) Pub Date : 2019-11-11
    Chiara Gabellieri, Marco Tognon, Dario Sanalitro, Lucia Pallottino, Antonio Franchi

    Cooperative manipulation is a basic skill in groups of humans, animals and in many robotic applications. Besides being an interesting challenge, communication-less approaches have been applied to groups of robots in order to achieve higher scalability and simpler hardware and software design. We present a generic model and control law for robots cooperatively manipulating an object, for both ground

    更新日期:2020-04-22
  • Sophisticated collective foraging with minimalist agents: a swarm robotics test
    Swarm Intell. (IF 2.556) Pub Date : 2019-10-10
    Mohamed S. Talamali, Thomas Bose, Matthew Haire, Xu Xu, James A. R. Marshall, Andreagiovanni Reina

    How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms

    更新日期:2020-04-22
  • Degrees of stochasticity in particle swarm optimization
    Swarm Intell. (IF 2.556) Pub Date : 2019-06-19
    E. T. Oldewage, A. P. Engelbrecht, C. W. Cleghorn

    This paper illustrates the importance of independent, component-wise stochastic scaling values, from both a theoretical and empirical perspective. It is shown that a swarm employing scalar stochasticity in the particle update equation is unable to express every point in the search space if the problem dimensionality is sufficiently large in comparison with the swarm size. The theoretical result is

    更新日期:2020-04-22
  • Coherent collective behaviour emerging from decentralised balancing of social feedback and noise
    Swarm Intell. (IF 2.556) Pub Date : 2019-09-04
    Ilja Rausch, Andreagiovanni Reina, Pieter Simoens, Yara Khaluf

    Decentralised systems composed of a large number of locally interacting agents often rely on coherent behaviour to execute coordinated tasks. Agents cooperate to reach a coherent collective behaviour by aligning their individual behaviour to the one of their neighbours. However, system noise, determined by factors such as individual exploration or errors, hampers and reduces collective coherence. The

    更新日期:2020-04-22
  • Multi-guide particle swarm optimization for multi-objective optimization: empirical and stability analysis
    Swarm Intell. (IF 2.556) Pub Date : 2019-08-19
    Christiaan Scheepers, Andries P. Engelbrecht, Christopher W. Cleghorn

    This article presents a new particle swarm optimization (PSO)-based multi-objective optimization algorithm, named multi-guide particle swarm optimization (MGPSO). The MGPSO is a multi-swarm approach, where each subswarm optimizes one of the objectives. An archive guide is added to the velocity update equation to facilitate convergence to a Pareto front of non-dominated solutions. An extensive empirical

    更新日期:2020-04-22
  • Collective decision making in dynamic environments
    Swarm Intell. (IF 2.556) Pub Date : 2019-06-26
    Judhi Prasetyo, Giulia De Masi, Eliseo Ferrante

    Collective decision making is the ability of individuals to jointly make a decision without any centralized leadership, but only relying on local interactions. A special case is represented by the best-of-n problem, whereby the swarm has to select the best option among a set of n discrete alternatives. In this paper, we perform a thorough study of the best-of-n problem in dynamic environments, in the

    更新日期:2020-04-22
  • Toward a theory of collective resource distribution: a study of a dynamic morphogenesis controller
    Swarm Intell. (IF 2.556) Pub Date : 2019-08-29
    Payam Zahadat, Daniel Nicolas Hofstadler

    Nature has various approaches to manage the collective distribution of resources. The division of a honeybee colony into subgroups, the formation of ant trails to food sources, and the spread of tree branches to optimize the access to light are some examples of collective decision making for resource distribution. This paper investigates collective distribution via an algorithm named vascular morphogenesis

    更新日期:2020-04-22
  • The intelligent water drops algorithm: why it cannot be considered a novel algorithm
    Swarm Intell. (IF 2.556) Pub Date : 2019-05-14
    Christian Leonardo Camacho-Villalón, Marco Dorigo, Thomas Stützle

    In this article, we rigorously analyze the intelligent water drops (IWD) algorithm, a metaphor-based approach for the approximate solution of discrete optimization problems proposed by Shah-Hosseini (in: Proceedings of the 2007 congress on evolutionary computation (CEC 2007), IEEE Press, Piscataway, NJ, pp 3226–3231, 2007). We demonstrate that all main algorithmic components of IWD are simplifications

    更新日期:2020-04-22
  • The PageRank algorithm as a method to optimize swarm behavior through local analysis
    Swarm Intell. (IF 2.556) Pub Date : 2019-08-23
    M. Coppola, J. Guo, E. Gill, G. C. H. E. de Croon

    This work proposes PageRank as a tool to evaluate and optimize the global performance of a swarm based on the analysis of the local behavior of a single robot. PageRank is a graph centrality measure that assesses the importance of nodes based on how likely they are to be reached when traversing a graph. We relate this, using a microscopic model, to a random robot in a swarm that transitions through

    更新日期:2020-04-22
  • Closed-loop task allocation in robot swarms using inter-robot encounters
    Swarm Intell. (IF 2.556) Pub Date : 2019-06-11
    Siddharth Mayya, Sean Wilson, Magnus Egerstedt

    In swarm robotics systems, coordinated behaviors emerge via local interactions among the robots as well as between robots and the environment. For a swarm of robots performing a set of pre-defined tasks in an enclosed region, this paper develops a decentralized mechanism to allocate tasks to each robot by leveraging the spatial interactions occurring among the robots as they move around the domain

    更新日期:2020-04-22
  • Long-term memory-induced synchronisation can impair collective performance in congested systems
    Swarm Intell. (IF 2.556) Pub Date : 2019-02-22
    F. Saffre, G. Gianini, H. Hildmann, J. Davies, S. Bullock, E. Damiani, J.-L. Deneubourg

    We investigate the hypothesis that long-term memory in populations of agents can lead to counterproductive emergent properties at the system level. Our investigation is framed in the context of a discrete, one-dimensional road-traffic congestion model: we investigate the influence of simple cognition in a population of rational commuter agents that use memory to optimise their departure time, taking

    更新日期:2020-04-22
  • A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem
    Swarm Intell. (IF 2.556) Pub Date : 2019-05-24
    Rim Zarrouk, Imed Eddine Bennour, Abderrazek Jemai

    Particle swarm optimization is a population-based stochastic algorithm designed to solve difficult optimization problems, such as the flexible job shop scheduling problem. This problem consists of scheduling a set of operations on a set of machines while minimizing a certain objective function. This paper presents a two-level particle swarm optimization algorithm for the flexible job shop scheduling

    更新日期:2020-04-22
  • Long-term pattern formation and maintenance for battery-powered robots
    Swarm Intell. (IF 2.556) Pub Date : 2019-02-04
    Guannan Li, Ivan Svogor, Giovanni Beltrame

    This paper presents a distributed, energy-aware method for the autonomous deployment and maintenance of battery-powered robots within a known or unknown region in 2D space. Our approach does not rely on a global positioning system and therefore allows for applications in GPS-denied environments such as underwater sensing or underground monitoring. After covering a region, our system maintains a formation

    更新日期:2020-04-22
  • Balancing robot swarm cost and interference effects by varying robot quantity and size
    Swarm Intell. (IF 2.556) Pub Date : 2018-12-10
    Adam Schroeder, Brian Trease, Alessandro Arsie

    Designing a robot swarm requires a swarm designer to understand the trade-offs unique to a swarm. The most basic design decisions are how many robots there should be in the swarm and the individual robot size. These choices in turn impact swarm cost and robot interference, and therefore swarm performance. The underlying physical reasons for why the number of robots and the individual robot size affect

    更新日期:2020-04-22
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