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  • Self-adaptive potential-based stopping criteria for Particle Swarm Optimization with forced moves
    Swarm Intell. (IF 2.556) Pub Date : 2020-09-10
    Bernd Bassimir, Manuel Schmitt, Rolf Wanka

    We study the variant of Particle Swarm Optimization that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain small bound in this dimension, arbitrarily set by the user. In this case, the swarm performs a forced move. In this paper, we are interested in how, by counting the forced moves, the swarm

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • Provable self-organizing pattern formation by a swarm of robots with limited knowledge
    Swarm Intell. (IF 2.556) Pub Date : 2019-02-11
    Mario Coppola; Jian Guo; Eberhard Gill; Guido C. H. E. de Croon

    In this paper we present a procedure to automatically design and verify the local behavior of robots with highly limited cognition. All robots are: anonymous, homogeneous, non-communicating, memoryless, reactive, do not know their global position, do not have global state information, and operate by a local clock. They only know: (1) the relative location of their neighbors within a short range and

  • 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

  • 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

  • Local information-based control for probabilistic swarm distribution guidance
    Swarm Intell. (IF 2.556) Pub Date : 2018-11-16
    Inmo Jang; Hyo-Sang Shin; Antonios Tsourdos

    This paper proposes a closed-loop decentralised framework for swarm distribution guidance, which disperses homogeneous agents over bins to achieve a desired density distribution by using feedback gains from the current swarm status. The key difference from existing works is that the proposed framework utilises only local information, not global information, to generate the feedback gains for stochastic

  • Balancing exploitation of renewable resources by a robot swarm
    Swarm Intell. (IF 2.556) Pub Date : 2018-07-27
    Roman Miletitch; Marco Dorigo; Vito Trianni

    Renewable resources like fish stock or forests should be exploited at a rate that supports regeneration and sustainability—a complex problem that requires adaptive approaches to maintain a sufficiently high exploitation while avoiding depletion. In the presence of oblivious agents that cannot keep track of all available resources—a frequent condition in swarm robotics—ensuring that the exploitation

  • Informative and misinformative interactions in a school of fish
    Swarm Intell. (IF 2.556) Pub Date : 2018-03-08
    Emanuele Crosato; Li Jiang; Valentin Lecheval; Joseph T. Lizier; X. Rosalind Wang; Pierre Tichit; Guy Theraulaz; Mikhail Prokopenko

    Quantifying distributed information processing is crucial to understanding collective motion in animal groups. Recent studies have begun to apply rigorous methods based on information theory to quantify such distributed computation. Following this perspective, we use transfer entropy to quantify dynamic information flows locally in space and time across a school of fish during directional changes around

  • On the role of collective sensing and evolution in group formation
    Swarm Intell. (IF 2.556) Pub Date : 2018-02-24
    Stefano Bennati

    Collective sensing is an emergent phenomenon which enables individuals to estimate a hidden property of the environment through the observation of social interactions. Previous work on collective sensing shows that gregarious individuals obtain an evolutionary advantage by exploiting collective sensing when competing against solitary individuals. This work addresses the question of whether collective

  • Kilogrid: a novel experimental environment for the Kilobot robot
    Swarm Intell. (IF 2.556) Pub Date : 2018-01-27
    Gabriele Valentini; Anthony Antoun; Marco Trabattoni; Bernát Wiandt; Yasumasa Tamura; Etienne Hocquard; Vito Trianni; Marco Dorigo

    We present the Kilogrid, an open-source virtualization environment and data logging manager for the Kilobot robot, Kilobot for short. The Kilogrid has been designed to extend the sensory-motor abilities of the Kilobot, to simplify the task of collecting data during experiments, and to provide researchers with a tool to fine-control the experimental setup and its parameters. Based on the design of the

  • Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor
    Swarm Intell. (IF 2.556) Pub Date : 2018-01-04
    Frank Bonnet; Alexey Gribovskiy; José Halloy; Francesco Mondada

    Collective behavior based on self-organization has been observed in populations of animals from insects to vertebrates. These findings have motivated engineers to investigate approaches to control autonomous multi-robot systems able to reproduce collective animal behaviors, and even to collectively interact with groups of animals. In this article, we show collective decision making by a group of autonomous

  • Putting it together: the computational complexity of designing robot controllers and environments for distributed construction
    Swarm Intell. (IF 2.556) Pub Date : 2017-12-20
    Todd Wareham; Andrew Vardy

    Creating target structures through the coordinated efforts of teams of autonomous robots (possibly aided by specific features in their environments) is a very important problem in distributed robotics. Many specific instances of distributed robotic construction teams have been developed manually. An important issue is whether automated controller design algorithms can both quickly produce robot controllers

  • An interactive agent-based framework for materialization-informed architectural design
    Swarm Intell. (IF 2.556) Pub Date : 2017-12-04
    Abel Groenewolt; Tobias Schwinn; Long Nguyen; Achim Menges

    Concepts of swarm intelligence are becoming increasingly relevant in the field of architectural design. An example is the use of agent-based modeling and simulation methods, which can help manage the complexity of building designs that feature many similar, but geometrically unique elements. Apart from leading to effective solutions and expanding the architectural design space, agent-based design methods

  • Self-adaptive particle swarm optimization: a review and analysis of convergence
    Swarm Intell. (IF 2.556) Pub Date : 2017-11-28
    Kyle Robert Harrison; Andries P. Engelbrecht; Beatrice M. Ombuki-Berman

    Particle swarm optimization (PSO) is a population-based, stochastic search algorithm inspired by the flocking behaviour of birds. The PSO algorithm has been shown to be rather sensitive to its control parameters, and thus, performance may be greatly improved by employing appropriately tuned parameters. However, parameter tuning is typically a time-intensive empirical process. Furthermore, a priori

  • Local force cues for strength and stability in a distributed robotic construction system
    Swarm Intell. (IF 2.556) Pub Date : 2017-11-23
    Nathan Melenbrink; Justin Werfel

    Construction of spatially extended, self-supporting structures requires a consideration of structural stability throughout the building sequence. For collective construction systems, where independent agents act with variable order and timing under decentralized control, ensuring stability is a particularly pronounced challenge. Previous research in this area has largely neglected considering stability

  • The Information-Cost-Reward framework for understanding robot swarm foraging
    Swarm Intell. (IF 2.556) Pub Date : 2017-11-17
    Lenka Pitonakova; Richard Crowder; Seth Bullock

    Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed

  • PolyACO+: a multi-level polygon-based ant colony optimisation classifier
    Swarm Intell. (IF 2.556) Pub Date : 2017-11-16
    Morten Goodwin; Torry Tufteland; Guro Ødesneltvedt; Anis Yazidi

    Ant colony optimisation (ACO) for classification has mostly been limited to rule-based approaches where artificial ants walk on datasets in order to extract rules from the trends in the data, and hybrid approaches which attempt to boost the performance of existing classifiers through guided feature reductions or parameter optimisations. A recent notable example that is distinct from the mainstream

  • An ant-inspired model for multi-agent interaction networks without stigmergy
    Swarm Intell. (IF 2.556) Pub Date : 2017-11-13
    Andreas Kasprzok; Beshah Ayalew; Chad Lau

    The aim of this work is to construct a microscopic model of multi-agent interaction networks inspired by foraging ants that do not use pheromone trails or stigmergic traces for communications. The heading and speed of each agent is influenced by direct interactions or encounters with other agents. Each agent moves in a plane using a correlated random walk whose probability distribution for heading

  • Stochastic stability of particle swarm optimisation
    Swarm Intell. (IF 2.556) Pub Date : 2017-11-09
    Adam Erskine; Thomas Joyce; J. Michael Herrmann

    Particle swarm optimisation (PSO) is a metaheuristic algorithm used to find good solutions in a wide range of optimisation problems. The success of metaheuristic approaches is often dependent on the tuning of the control parameters. As the algorithm includes stochastic elements that effect the behaviour of the system, it may be studied using the framework of random dynamical systems (RDS). In PSO,

  • Can individual heterogeneity influence self-organised patterns in the termite nest construction model?
    Swarm Intell. (IF 2.556) Pub Date : 2017-10-28
    Fabrice Saffre; Hanno Hildmann; Jean-Louis Deneubourg

    We present investigations on the influence of individual heterogeneity on self-organised patterns in the termite nest construction model. The presented results extend the original model (Bruinsma 1979; Deneubourg 1977) from theoretical biology which has served as foundation and inspiration for computational optimisation approaches. Our findings have implications for the handling of heterogeneities

  • Reinforcement learning in a continuum of agents
    Swarm Intell. (IF 2.556) Pub Date : 2017-10-13
    Adrian Šošić; Abdelhak M. Zoubir; Heinz Koeppl

    We present a decision-making framework for modeling the collective behavior of large groups of cooperatively interacting agents based on a continuum description of the agents’ joint state. The continuum model is derived from an agent-based system of locally coupled stochastic differential equations, taking into account that each agent in the group is only partially informed about the global system

  • Particle swarm stability: a theoretical extension using the non-stagnate distribution assumption
    Swarm Intell. (IF 2.556) Pub Date : 2017-09-27
    Christopher W. Cleghorn; Andries P. Engelbrecht

    This paper presents an extension of the state of the art theoretical model utilized for understanding the stability criteria of the particles in particle swarm optimization algorithms. Conditions for order-1 and order-2 stability are derived by modeling, in the simplest case, the expected value and variance of a particle’s personal and neighborhood best positions as convergent sequences of random variables

  • Continuous time gathering of agents with limited visibility and bearing-only sensing
    Swarm Intell. (IF 2.556) Pub Date : 2017-08-23
    Levi Itzhak Bellaiche; Alfred Bruckstein

    A group of mobile agents, identical, anonymous, and oblivious (memoryless), able to sense only the direction (bearing) to neighboring agents within a finite visibility range, are shown to gather to a meeting point, in finite time, by applying a very simple rule of motion. The agents act in continuous time, and their rule of motion is as follows: they determine the smallest visibility disk sector in

  • Automatic synthesis of rulesets for programmable stochastic self-assembly of rotationally symmetric robotic modules
    Swarm Intell. (IF 2.556) Pub Date : 2017-08-17
    Bahar Haghighat; Alcherio Martinoli

    Programmable stochastic self-assembly of modular robots provides promising means to formation of structures at different scales. One way to address the design of dedicated control rulesets for self-assembling robotic modules is to leverage formalisms based on graph grammar. While these tools are powerful and allow for formal analysis of the resulting controllers, expressing the embodiment of the robotic

  • Learning cluster-based classification systems with ant colony optimization algorithms
    Swarm Intell. (IF 2.556) Pub Date : 2017-07-24
    Khalid M. Salama; Ashraf M. Abdelbar

    Classification is a data mining task the goal of which is to learn a model, from a training dataset, that can predict the class of a new data instance, while clustering aims to discover natural instance-groupings within a given dataset. Learning cluster-based classification systems involves partitioning a training set into data subsets (clusters) and building a local classification model for each data

  • The impact of agent density on scalability in collective systems: noise-induced versus majority-based bistability
    Swarm Intell. (IF 2.556) Pub Date : 2017-05-17
    Yara Khaluf; Carlo Pinciroli; Gabriele Valentini; Heiko Hamann

    In this paper, we show that non-uniform distributions in swarms of agents have an impact on the scalability of collective decision-making. In particular, we highlight the relevance of noise-induced bistability in very sparse swarm systems and the failure of these systems to scale. Our work is based on three decision models. In the first model, each agent can change its decision after being recruited

  • Optimal information transfer and stochastic resonance in collective decision making
    Swarm Intell. (IF 2.556) Pub Date : 2017-04-12
    Bernd Meyer

    Self-organised collective decision making is one of the core components of swarm intelligence, and numerous swarm algorithms that are widely used in optimisation and optimal control have been inspired by the biological mechanisms driving it. Beyond the life sciences and bio-inspired engineering, collective decision making is important in a number of other disciplines, most prominently economics and

  • Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies
    Swarm Intell. (IF 2.556) Pub Date : 2017-03-31
    Muhanad H. Mohammed Alkilabi; Aparajit Narayan; Elio Tuci

    Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory

  • An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study
    Swarm Intell. (IF 2.556) Pub Date : 2017-03-30
    Olacir R. Castro; Aurora Pozo; Jose A. Lozano; Roberto Santana

    A variety of general strategies have been applied to enhance the performance of multi-objective optimization algorithms for many-objective optimization problems (those with more than three objectives). One of these strategies is to split the solutions to cover different regions of the search space (clusters) and apply an optimizer to each region with the aim of producing more diverse solutions and

  • ABC-X: a generalized, automatically configurable artificial bee colony framework
    Swarm Intell. (IF 2.556) Pub Date : 2017-02-21
    Doğan Aydın; Gürcan Yavuz; Thomas Stützle

    The artificial bee colony (ABC) algorithm is a popular metaheuristic that was originally conceived for tackling continuous function optimization tasks. Over the last decade, a large number of variants of ABC have been proposed, making it by now a well-studied swarm intelligence algorithm. Typically, in a paper on algorithmic variants of ABC algorithms, one or at most two of its algorithmic components

  • A new indicator-based many-objective ant colony optimizer for continuous search spaces
    Swarm Intell. (IF 2.556) Pub Date : 2017-02-20
    Jesús Guillermo Falcón-Cardona; Carlos A. Coello Coello

    In this paper, we propose a novel multi-objective ant colony optimizer (called iMOACO\(_{\mathbb {R}}\)) for continuous search spaces, which is based on ACO\(_{\mathbb {R}}\) and the R2 performance indicator. iMOACO\(_{\mathbb {R}}\) is the first multi-objective ant colony optimizer (MOACO) specifically designed to tackle continuous many-objective optimization problems (i.e., multi-objective optimization

  • Efficient spatial coverage by a robot swarm based on an ant foraging model and the Lévy distribution
    Swarm Intell. (IF 2.556) Pub Date : 2017-02-17
    Adam Schroeder; Subramanian Ramakrishnan; Manish Kumar; Brian Trease

    This work proposes a control law for efficient area coverage and pop-up threat detection by a robot swarm inspired by the dynamical behavior of ant colonies foraging for food. In the first part, performance metrics that evaluate area coverage in terms of characteristics such as rate, completeness and frequency of coverage are developed. Next, the Keller–Segel model for chemotaxis is adapted to develop

  • Searching for structural bias in particle swarm optimization and differential evolution algorithms
    Swarm Intell. (IF 2.556) Pub Date : 2016-11-14
    Adam P. Piotrowski; Jaroslaw J. Napiorkowski

    During the last two decades, a large number of metaheuristics have been proposed, leading to various studies that call for a deeper insight into the behaviour, efficiency and effectiveness of such methods. Among numerous concerns that are briefly reviewed in this paper, the presence of a structural bias (i.e. the tendency, not justified by the fitness landscape, to visit some regions of the search

  • Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms
    Swarm Intell. (IF 2.556) Pub Date : 2016-11-03
    Luca Mondada; Mohammad Ehsanul Karim; Francesco Mondada

    Search and rescue, autonomous construction, and many other semi-autonomous multirobot applications can benefit from proximal interactions between an operator and a swarm of robots. Most research on proximal interaction is based on explicit communication techniques such as gesture and speech. This study proposes a new implicit proximal communication technique to approach the problem of robot selection

  • Inertia weight control strategies for particle swarm optimization
    Swarm Intell. (IF 2.556) Pub Date : 2016-11-02
    Kyle Robert Harrison; Andries P. Engelbrecht; Beatrice M. Ombuki-Berman

    Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia

  • Turing learning: a metric-free approach to inferring behavior and its application to swarms
    Swarm Intell. (IF 2.556) Pub Date : 2016-08-30
    Wei Li; Melvin Gauci; Roderich Groß

    We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the

  • A new particle swarm optimization algorithm for noisy optimization problems
    Swarm Intell. (IF 2.556) Pub Date : 2016-07-09
    Sajjad Taghiyeh; Jie Xu

    We propose a new particle swarm optimization algorithm for problems where objective functions are subject to zero-mean, independent, and identically distributed stochastic noise. While particle swarm optimization has been successfully applied to solve many complex deterministic nonlinear optimization problems, straightforward applications of particle swarm optimization to noisy optimization problems

  • Investigating the effect of increasing robot group sizes on the human psychophysiological state in the context of human–swarm interaction
    Swarm Intell. (IF 2.556) Pub Date : 2016-06-22
    Gaëtan Podevijn; Rehan O’Grady; Nithin Mathews; Audrey Gilles; Carole Fantini-Hauwel; Marco Dorigo

    We study the psychophysiological state of humans when exposed to robot groups of varying sizes. In our experiments, 24 participants are exposed sequentially to groups of robots made up of 1, 3 and 24 robots. We measure both objective physiological metrics (skin conductance level and heart rate), and subjective self-reported metrics (from a psychological questionnaire). These measures allow us to analyse

  • Medoid-based clustering using ant colony optimization
    Swarm Intell. (IF 2.556) Pub Date : 2016-05-09
    Héctor D. Menéndez; Fernando E. B. Otero; David Camacho

    The application of ACO-based algorithms in data mining has been growing over the last few years, and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works about unsupervised learning have focused on clustering, showing the potential of ACO-based techniques. However, there are still clustering areas that are almost unexplored

  • Modeling multi-robot task allocation with limited information as global game
    Swarm Intell. (IF 2.556) Pub Date : 2016-04-28
    Anshul Kanakia; Behrouz Touri; Nikolaus Correll

    Continuous response threshold functions to coordinate collaborative tasks in multi-agent systems are commonly employed models in a number of fields including ethology, economics, and swarm robotics. Although empirical evidence exists for the response threshold model in predicting and matching swarm behavior for social insects, there has been no formal argument as to why natural swarms use this approach

  • A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
    Swarm Intell. (IF 2.556) Pub Date : 2016-03-28
    Enrico Ampellio; Luca Vassio

    In many technical fields, single-objective optimization procedures in continuous domains involve expensive numerical simulations. In this context, an improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide fast convergence speed, high solution accuracy and robust performance over a wide range of problems

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