Swarm Members Research Articles

A hybrid modified step Whale Optimization Algorithm with Tabu Search for data clustering

Clustering is a popular data analysis tool that groups similar data objects into disjoint clusters. Clustering algorithms have major drawbacks, such as: sensitivity to initialization and getting trapped in local optima. Swarm Intelligence (SI) have been combined with clustering techniques to enhance their performance. Despite their improvements, some SI methods, such as Particle Swarm Optimization (PSO) and its rivals, had their drawbacks, such as low convergence rates and generating low quality solutions. This is due to storing a single best solution about solution space, which can lead swarm members to local optima. Whale Optimization Algorithm (WOA) is a recent SI method, which has been proved as a global optimizer over multiple optimization problems. Unlikely, WOA uses the same concept as PSO, it uses the best solution to reposition swarm members. To overcome this problem, WOA needs to store multiple best solutions about solution space. Tabu Search (TS) is a meta-heuristic method, which uses memory components to explore and exploit search space. In this paper, we propose combining WOA with TS (WOATS) for data clustering. To assess the efficiency of WOATS, it has been applied in data clustering. WOATS uses an objective function inspired by partitional clustering to maintain the quality of clustering solutions. WOATS was tested over multiple real life datasets, which their sizes vary from small to large-sized datasets. WOATS was able to find centers with high quality in a small number of iterations regardless of the size of the dataset, which clarifies its ability to cover the solution space efficiently. The generated clusters were evaluated according to different outlier criteria: Davies-Bouldin Index and Dunn Index. According to these criteria, WOATS presented its superiority over multiple recent original and hybrid SI methods for different datasets. Results obtained by WOATS were better than the results of the other methods with big differences regarding to the different outlier criteria, which ensured the efficiency of WOATS.

A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems

Cloud computing has attracted great attentions in research community because of its ubiquitous, unlimited computing resources, low cost, and flexibility owing to virtualization technology. This paper presents a hybrid meta-heuristic algorithm to solve parallelizable scientific workflows on elastic cloud platforms since applying a single approach cannot yield optimal solution in such complicated problems. Scientific workflows are modeled in the form of directed acyclic graph (DAG) in which there exists data dependency between sub-tasks. In the cloud marketplace, each provider delivers variable virtual machine (VM) configurations which lead different performance. Generally, parallelizable task scheduling on parallel computing machines to obtain minimum total execution time, makespan, belongs to NP-Hard problem. To deal with the combinatorial problem, the hybrid discrete particle swarm optimization (HDPSO) algorithm is presented that has three main phases. At the first phase a random algorithm following by novel theorems is applied to produce swarm members; it is as input of presented new discrete particle swarm optimization (DPSO) algorithm in the second phase. To avoid getting stuck in sub-optimal trap and to balance between exploration and exploitation, local search improvement is randomly combined in DPSO by calling Hill Climbing technique at the third phase to enhance overall performance. Second and third phases are iterated till the termination criteria is met. The average results reported from different executions of intensive settings on 12 scientific datasets proved our hybrid meta-heuristic has the amount of 10.67, 14.48, and 3 percentage dominance in terms of SLR, SpeedUp, and efficiency respectively against other existing meta-heuristics.

Bio-inspired Coordination and Control of Autonomous Vehicles in Future Manufacturing and Goods Transportation

Abstract The will to apply bio-inspired techniques to coordinate and control autonomous X vehicles (AXVs) has increased tremendously during the last decade due to their advantages in the face of complexity in today’s demanding applications. Thus, several bio-inspired approaches for multiple-entities optimization have been proposed in the literature for various limited applications, e.g., drone coordination, mobile robot formation maintenance. In all these strategies, the entities must plan their path and control their movements while coordinating their behavior w.r.t. the other members, and they must avoid collisions, so the task could be very difficult in the unstructured environments present in future manufacturing plants and goods transportation. Future applications of these bio-inspired techniques for coordination and control of AXVs include large warehouses, manufacturing, logistics, last-mile delivery, etc. The AXVs could be grouped to carry larger goods or they can act as swarm members when they do not have a common goal, but they must interact while they move to complete the allocated tasks and intersect their paths with the paths of other entities. As such, this paper illustrates the concept of applying such bio-inspired coordination and control techniques for the development of future manufacturing and goods transportation, a discussion being carried out regarding the advantages and disadvantages of several techniques for their use in specific applications.

Satellite swarm survey and new conceptual design for Earth observation applications

The new trend in Earth observation applications is to use a set of low-cost, simple and short development time satellites. A Swarm mission of twelve CubeSats as imaging nodes, besides, hub Microsatellite was already proposed to fulfill the earth observation mission called Fractionated Imaging Satellite Cluster (FIC). The weakest point in that proposal was the using of an optical communication link between the imaging node satellites and one hub satellite to transfer a huge amount of imaging data. As optical communication needs a highly accurate attitude control system that exceeds the capability of CubeSats, and it is not commercially available yet. This paper gives a survey of the previously published swarm mission and it gives a proposed configuration of satellite swarm that allows using commercially available radio-frequency communication subsystems between the swarm members to fulfill the required mission.

A Pareto-Based Hybrid Whale Optimization Algorithm with Tabu Search for Multi-Objective Optimization

Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.

Robots swarm communication control based on biological behavior inspiration

<p>Robot swarming, increasingly find importance in the last decade. In these systems, multi mobile robots have to work cooperatively to perform specified tasks. One of the compelling problems is that the robots movements should be in such a way that they should follow a specific guide and at the same time they should have the ability of obstacle avoidance. Inspiriting such movement from biological swarms is a compelling problem. Fish schools, bird flocks and sheep herds are particular examples of biological systems swarming. In this paper, a robot swarming algorithm was developed based on swarming rules noticed in these biological systems, the combination between the swarm members and the leadership control also explained, an ad-hoc non-essential communication system was proposed for the purpose of use in case of collective takeoff and collective landing swarm-robots, in which activated automatically.</p>

Distributed aerial processing for IoT-based edge UAV swarms in smart farming

This work addresses the challenges of a decentralized and heterogeneous Unmanned Aerial Vehicle (UAV) swarm deployment – some fitted with multimedia sensors, while others armed with scalar sensors – in resource-constrained and challenging environments, typically associated with farming. Subsequently, we also address the resulting problem of sensing and processing resource-intensive data aerially within the Edge swarm in the fastest and most efficient manner possible. The heterogeneous nature of the Edge swarm results in under-utilization of the available computation resources due to unequal data generation within its members. To address this, we propose a Nash bargaining-based weighted intra-Edge processing offload scheme to mitigate the problem of heavy processing in some of the swarm members. We do this by distributing the data to be processed to all the swarm members. Real-life hardware tuned simulation of a large UAV swarm shows that by increasing the number of UAVs in the swarm, our scheme achieves better scalability and reduced processing delays for intensive processing tasks. Additionally, in comparison to regular star and mesh topologies, our scheme achieves an increase in collective available network processing speeds by 100% for only 25% of the number of UAVs in a star topology.

Learning Vision-Based Flight in Drone Swarms by Imitation

Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This letter proposes an entirely visual approach to coordinate markerless drone swarms based on imitation learning. Each agent is controlled by a small and efficient convolutional neural network that takes raw omnidirectional images as inputs and predicts 3D velocity commands that match those computed by a flocking algorithm. We start training in simulation and propose a simple yet effective unsupervised domain adaptation approach to transfer the learned controller to the real world. We further train the controller with data collected in our motion capture hall. We show that the convolutional neural network trained on the visual inputs of the drone can learn not only robust inter-agent collision avoidance but also cohesion of the swarm in a sample-efficient manner. The neural controller effectively learns to localize other agents in the visual input, which we show by visualizing the regions with the most influence on the motion of an agent. We remove the dependence on sharing positions among swarm members by taking only local visual information into account for control. Our work can therefore be seen as the first step towards a fully decentralized, vision-based swarm without the need for communication or visual markers.

A Multiband Biconical Log-periodic Antenna for Swarm Communications

In this paper, we present a specific multiband antenna design that addresses the problem of communication with unmanned aerial vehicles (UAVs). We consider a scenario where multiband single-antenna UAV communicates with the rest of the swarm members that are equipped with similar antennas. The key point in the design is that the communication does not require high or low elevation angles in most of the cases. The suggested design has a sufficient degree of freedom to select the desired features for the field pattern while keeping other features such as antenna impedance and gain relatively stable or at least in the acceptable operation region.

Resource-Optimized Multiarmed Bandit-Based Offload Path Selection in Edge UAV Swarms

This paper looks into the problem of a decentralized data offloading within an edge unmanned aerial vehicle (UAV) swarm to mitigate the complexities of a single UAV continually generating and processing large application-specific data. The mobile edge UAVs considered here are multirotor types having constrained energy and processing power, which makes long-term handling of large data volumes impossible for standalone UAVs. The load mitigation is carried out by offloading data from a source UAV to other swarm members with sufficient energy and processing requirements. In this paper, we focus on selecting the most optimal multihop path through the UAVs concerning available energies and processing resources, which can survive the duration of the data offload between the source and a target UAV. We formulate a multiarmed bandit-based offload path selection scheme, which selects the most energy and processing optimized multihop path between a source and a target UAV. Upon comparison of our scheme against the naive shortest path approach, we observe that our approach results in significant savings of collective network energies, even for long operational durations.

Motion optimization algorithm designing for swarm quadrotors in application of grasping objects

In this study, the process of designing a motion optimization algorithm for swarm quadrotor robots is presented. Motions equations of swarm are written based on Lagrangian energy equations. A potential function is applied on the equations to optimize the swarm motion. The applied potential function enables each of the swarm members to move toward an independent target coordinate as motion starts and simultaneously connecting with other members. As a result, the necessity of having the members aggregated within an area close to the swarm center is eliminated. This algorithm is supposed to act on swarm of quadrotors; therefore a validated dynamic model of quadrotor and a designed controller are introduced to discuss the possible applications. The designed algorithm is then applied to grasp an object. In order to establish grasping, particle swarm optimization method is used. Finally, the algorithm is simulated in MATLAB for a two-member swarm of quadrotors for grasping the object. Simulation results indicate increased work space for the members along the motion path and reduced mission time.

How swarming will change warfare

ABSTRACTWith advances in artificial intelligence and machine autonomy, a profound transformation in warfare is underway. The convergence of these technologies is enabling drone swarms, made up of cooperative, autonomous robots that react to the battlefield as one. Much as they do in colonies of insects, flocks of birds, or packs of wolves, swarm members will work together to create a unified whole that is greater than the sum of the individuals. They will allow militaries to field forces that are larger in number, faster, and better coordinated – not to mention more expendable – than would be possible with humans alone. Robotic swarms will benefit some countries more than others, decoupling military power from personnel limitations. The most profound and long-lasting impact of swarms will be in the way they change command-and-control, that is, the way militaries organize and fight on the battlefield.

A Kinematic Model for Swarm Finite-Time Trajectory Tracking.

This paper focuses on the trajectory tracking problem for a swarm of mobile agents. A kinematic model describing the interactions and evolutions of the swarm members is proposed and its main properties are analyzed emphasizing that the agents centroid is ensured to track in finite-time a given reference trajectory and that the agents reach an aggregation in finite-time in a hyper-ball moving around the centroid path. One of the main characteristics of the model is the presence of an interaction matrix, between agents coordinates, which allows to define some properties of the swarm allowing the creation of different forms of agents aggregations, i.e., spheres, ellipsoids, straight lines, etc. Indeed swarm properties related to the agents configuration around the performed path along with agents interactions and absence of collisions are analyzed depending on the chosen interaction matrix.

Data Transfer via UAV Swarm Behaviours

This paper presents an adaptive robotic swarm of Unmanned Aerial Vehicles (UAVs) enabling communications between separated non-swarm devices. The swarm nodes utilise machine learning and hyper-heuristic rule evolution to enable each swarm member to act appropriately for the given environment. The contribution of the machine learning is verified with an exploration of swarms with and without this module. The exploration finds that in challenging environments the learning greatly improves the swarm’s ability to complete the task.
 The swarm evolution process of this study is found to successfully create different data transfer methods depending on the separation of non-swarm devices and the communication range of the swarm members. This paper also explores the resilience of the swarm to agent loss, and the scalability of the swarm in a range of environment sizes. In regard to resilience, the swarm is capable of recovering from agent loss and is found to have improved evolution. In regard to scalability, the swarm is observed to have no upper limit to the number of agents deployed in an environment. However, the size of the environment is seen to be a limit for optimal swarm performance.

Data Transfer via UAV Swarm Behaviours

This paper presents an adaptive robotic swarm of Unmanned Aerial Vehicles (UAVs) enabling communications between separated non-swarm devices. The swarm nodes utilise machine learning and hyper-heuristic rule evolution to enable each swarm member to act appropriately for the given environment. The contribution of the machine learning is verified with an exploration of swarms with and without this module. The exploration finds that in challenging environments the learning greatly improves the swarm’s ability to complete the task.
 The swarm evolution process of this study is found to successfully create different data transfer methods depending on the separation of non-swarm devices and the communication range of the swarm members. This paper also explores the resilience of the swarm to agent loss, and the scalability of the swarm in a range of environment sizes. In regard to resilience, the swarm is capable of recovering from agent loss and is found to have improved evolution. In regard to scalability, the swarm is observed to have no upper limit to the number of agents deployed in an environment. However, the size of the environment is seen to be a limit for optimal swarm performance.

VORTEX OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM

In this paper, a new Vortex Optimization (VO) algorithm is proposed to solve the reactive power problem. The idea is generally focused on a typical Vortex flow in nature and enthused from some dynamics that are occurred in the sense of Vortex nature. In a few words, the algorithm is also a swarm-oriented evolutional problem solution methodology; since it comprises numerous techniques related to removal of feeble swarm members and trying to progress the solution procedure by supporting the solution space through fresh swarm members. In order to evaluate the performance of the proposed Vortex Optimization (VO) algorithm, it has been tested in Standard IEEE 30 bus systems and compared to other standard algorithms. Simulation results reveal about the best performance of the proposed algorithm in reducing the real power loss and static voltage stability margin index has been enhanced.

Oceanic swarms of Antarctic krill perform satiation sinking.

Antarctic krill form some of the highest concentrations of animal biomass observed in the world's oceans potentially due to their prolific ability to swarm. Determining the movement of Antarctic krill within swarms is important to identify drivers of their behaviour and their biogeochemical impact on their environment. We examined vertical velocity within approximately 2000 krill swarms through the combined use of a shipborne echosounder and an acoustic Doppler current profiler. We revealed a pronounced downward anomaly in vertical velocity within swarms of -0.6 cm s-1 compared with vertical motion outside the swarm. The anomaly changed over the diel cycle, with smaller downward anomalies occurring at night. Swarms in regions of high phytoplankton concentrations (a proxy for food availability) also exhibited significantly smaller downward anomalies. We propose that the anomaly is the result of downward velocities generated by the action of krill beating their swimming appendages. During the night and in high phytoplankton availability, when krill are more likely to feed to the point of satiation, swimming activity is lowered and the anomaly is reduced. Our findings are consistent with laboratory work where krill ceased swimming and adopted a parachute posture when sated. Satiation sinking behaviour can substantially increase the efficiency of carbon transport to depth through depositing faecal pellets at the bottom of swarms, avoiding the reingestion and break-up of pellets by other swarm members.

Model of the best-of-N nest-site selection process in honeybees.

The ability of a honeybee swarm to select the best nest site plays a fundamental role in determining the future colony's fitness. To date, the nest-site selection process has mostly been modeled and theoretically analyzed for the case of binary decisions. However, when the number of alternative nests is larger than two, the decision-process dynamics qualitatively change. In this work, we extend previous analyses of a value-sensitive decision-making mechanism to a decision process among N nests. First, we present the decision-making dynamics in the symmetric case of N equal-quality nests. Then, we generalize our findings to a best-of-N decision scenario with one superior nest and N-1 inferior nests, previously studied empirically in bees and ants. Whereas previous binary models highlighted the crucial role of inhibitory stop-signaling, the key parameter in our new analysis is the relative time invested by swarm members in individual discovery and in signaling behaviors. Our new analysis reveals conflicting pressures on this ratio in symmetric and best-of-N decisions, which could be solved through a time-dependent signaling strategy. Additionally, our analysis suggests how ecological factors determining the density of suitable nest sites may have led to selective pressures for an optimal stable signaling ratio.

Wavelike Patterns in Precessing Elliptical Rings for Swarming Systems

A continuum model for a swarm of devices is investigated with the devices moving along precessing elliptical Earth-centered orbits. Wavelike patterns in these precessing elliptical rings with peaks in swarm density are found that can be used to provide enhanced coverage for Earth observation and space science. Two orbital models are considered for the purpose of comparison: perturbed by and solar radiation pressure, and perturbed by and , respectively, each with a different frozen eccentricity. By removing osculating orbital elements, only the long-period orbit eccentricity and argument of perigee are chosen to derive closed-form solutions to the continuum model for the swarm density. Zero-density lines in the swarm density are found, as well as infinite density at certain boundaries. Comparison between the analytic and numerical number density evolutions is made to yield the range of applicable eccentricity based on the maximum error tolerance, as well as the minimum number of swarm members required to approximate continuous evolution. Closed-form solutions are then derived to predict the number density of swarm devices for magnetic-tail measurement and Earth-observation applications.

Implementation of Swarm Social Foraging Behavior in Unmanned Aerial Vehicle (UAV) Quadrotor Swarm

One of the novel approaches in multiple quadrotor control is swarm robotics. It aims to mimic social behaviors of animals and insects. This paper presents the physical implementation of the swarm social foraging behavior in unmanned aerial vehicle quadrotors. To achieve this, it first explores the basic behavior of aggregation. It is implemented over a quadrotor swarm test-bed that makes use of external motion capture cameras. The completed algorithm makes use of the artificial potential function model combined with the environment resource profile model. Results show successful demonstration of the social foraging algorithm with minimal error in position. Also, the proposed algorithm’s performance presents an increase in aggregation speed and time as the number of swarm member increases.