Natural Swarms Research Articles

Cooperative Velocity Updating model based Particle Swarm Optimization

Particle swarm optimization (PSO) has shown its good performance in many optimization problems. This paper proposes a Cooperative Velocity Updating algorithm based Particle Swarm Optimization (CVUPSO), which is inspired by the competition and cooperation methods of different populations in natural swarm living, such as bees, ants, birds, fish, etc. In this algorithm, before an elite is introduced from other sub-swarms or a new particle is randomly born, the weak particle will be eliminated out of its sub-swarm. In each iteration process, every sub-swarm abandons a least potential particle. The CVUPSO recorded four special positions: pbest, lbest, gbest and lworst. The pbest represents the current particle's best position while lbest represents the current sub swarm's best position, and gbest is the best position among the whole swarm, lworst is the position of the particle with the worst performance. A new update method is adopted in CVUPSO, where the particles are more likely to follow lbest than follow gbest in the early stage of iteration, but opposite in the later stage. In this paper, two variants of CVUPSO are proposed, one variant is CVUPSO with Random inertia weight (for short CVUPSO-R), and another is CVUPSO with Exponential decline inertia weight (for short CVUPSO-E). By making comparative experiments on several widely used benchmark functions, analysis results show that the performance of these two improved variants are more promising than the recently developed PSO algorithms for searching multiple peak values of multiple objects optimization problem.

Cooperative Problem Solving in Telecommunication Network

Swarm intelligence, as demonstrated by natural biological swarms, has numerous powerful properties desirable in many engineering systems, such as telecommunication. Communication network management is becoming increasingly difficult due to the increasing size, rapidly changing topology, and complexity of communication networks. This paper describes how biologically-inspired agents can be used to solve control problems in telecommunications. These agents, inspired by the foraging behaviour of ants, exhibit the desirable characteristics of simplicity of action and interaction. The colle ction of agents, or swarm system, deals only with local knowledge and exhibits a form of distributed control with agent communication effected through the environment. In this paper we explore the application of ant-like agents to the problem of routing in telecommunication networks.

Towards swarm calculus: urn models of collective decisions and universal properties of swarm performance

Methods of general applicability are searched for in swarm intelligence with the aim of gaining new insights about natural swarms and to develop design methodologies for artificial swarms. An ideal solution could be a ‘swarm calculus’ that allows to calculate key features of swarms such as expected swarm performance and robustness based on only a few parameters. To work towards this ideal, one needs to find methods and models with high degrees of generality. In this paper, we report two models that might be examples of exceptional generality. First, an abstract model is presented that describes swarm performance depending on swarm density based on the dichotomy between cooperation and interference. Typical swarm experiments are given as examples to show how the model fits to several different results. Second, we give an abstract model of collective decision making that is inspired by urn models. The effects of positive-feedback probability, that is increasing over time in a decision making system, are understood by the help of a parameter that controls the feedback based on the swarm’s current consensus. Several applicable methods, such as the description as Markov process, calculation of splitting probabilities, mean first passage times, and measurements of positive feedback, are discussed and applications to artificial and natural swarms are reported.

Algorithmic requirements for swarm intelligence in differently coupled collective systems

Swarm systems are based on intermediate connectivity between individuals and dynamic neighborhoods. In natural swarms self-organizing principles bring their agents to that favorable level of connectivity. They serve as interesting sources of inspiration for control algorithms in swarm robotics on the one hand, and in modular robotics on the other hand. In this paper we demonstrate and compare a set of bio-inspired algorithms that are used to control the collective behavior of swarms and modular systems: BEECLUST, AHHS (hormone controllers), FGRN (fractal genetic regulatory networks), and VE (virtual embryogenesis). We demonstrate how such bio-inspired control paradigms bring their host systems to a level of intermediate connectivity, what delivers sufficient robustness to these systems for collective decentralized control. In parallel, these algorithms allow sufficient volatility of shared information within these systems to help preventing local optima and deadlock situations, this way keeping those systems flexible and adaptive in dynamic non-deterministic environments.

Stochastic Diffusion Search Review

AbstractStochastic Diffusion Search, first incepted in 1989, belongs to the extended family of swarm intelligence algorithms. In contrast to many nature-inspired algorithms, stochastic diffusion search has a strong mathematical framework describing its behaviour and convergence. In addition to concisely exploring the algorithm in the context of natural swarm intelligence systems, this paper reviews various developments of the algorithm, which have been shown to perform well in a variety of application domains including continuous optimisation, implementation on hardware and medical imaging. This algorithm has also being utilised to argue the potential computational creativity of swarm intelligence systems through the two phases of exploration and exploitation.

Shear wave velocity models retrieved using Rg wave dispersion data in shallow crust in some regions of southern Ontario, Canada

Many crucial tasks in seismology, such as locating seismic events and estimating focal mechanisms, need crustal velocity models. The velocity models of shallow structures are particularly important in the simulation of ground motions. In southern Ontario, Canada, many small shallow earthquakes occur, generating high-frequency Rayleigh (Rg) waves that are sensitive to shallow structures. In this research, the dispersion of Rg waves was used to obtain shear-wave velocities in the top few kilometers of the crust in the Georgian Bay, Sudbury, and Thunder Bay areas of southern Ontario. Several shallow velocity models were obtained based on the dispersion of recorded Rg waves. The Rg waves generated by an mN 3.0 natural earthquake on the northern shore of Georgian Bay were used to obtain velocity models for the area of an earthquake swarm in 2007. The Rg waves generated by a mining induced event in the Sudbury area in 2005 were used to retrieve velocity models between Georgian Bay and the Ottawa River. The Rg waves generated by the largest event in a natural earthquake swarm near Thunder Bay in 2008 were used to obtain a velocity model in that swarm area. The basic feature of all the investigated models is that there is a top low-velocity layer with a thickness of about 0.5 km. The seismic velocities changed mainly within the top 2 km, where small earthquakes often occur.

Swarm Flooding Attack against Directed Diffusion in Wireless Sensor Networks

The objective of this paper is to study the vulnerabilities of sensor networks, design, and implement new approaches for routing attack. As one of the cornerstones of network infrastructure, routing systems are facing more threats than ever; they are vulnerable by nature and challenging to protect. We present a new attack, Swarm Flooding Attack, against Directed Diffusion based WSNs, which targets the consumption of sensors computational resources, such as bandwidth, disk space, or processor time. Two variants of swarm attack have been introduced: Bee and Ant. Both approaches are inspired from the natural swarming difference between bees and ants. In all cases, the strategy used to mount an attack is the same. An attack consists of a set of malicious user queries represented by interests that are inserted into the network. However, the two forms of attack vary in the synchronization aspects among attackers. These types of attacks are hard to defend against as illustrated. For each of the proposed attack models, we present analysis, simulation, and experimental measurements. We show that the system achieves maximal damage on system performance represented by many metrics.

Gravitational Search Algorithm Based Optimal Reactive Power Dispatch for Voltage Stability Enhancement

In this article, an efficient and reliable optimization procedure based on the behaviors of swarm in nature, namely the gravitational search algorithm, is proposed for solving multi-objective optimal reactive power dispatch problems, which minimizes transmission loss while maintaining the quality of voltages. The gravitational search algorithm is based on Newton's law of gravity and interaction of masses. In the proposed algorithm, the searcher agents which are a collection of masses interact with each other using Newton's laws of gravity and motion. In order to investigate the performance of the proposed scheme, multi-objective optimal reactive power dispatch problems are solved. This new gravitational search algorithm method is tested on IEEE 57-bus and IEEE 118-bus power systems. Results obtained by the gravitational search algorithm are compared with two versions of genetic algorithms, three versions of differential evolution algorithms, four versions of particle swarm optimization algorithms and the seeker optimization algorithm It is observed from the test results that the proposed gravitational search algorithm approach converges to better solutions much faster than the earlier reported approaches.

Modelling the swarm: Analysing biological and engineered swarm systems

In this article, we discuss the current research efforts on swarm systems and on the motivation of mathematical modelling of such distributed and self-organizing systems. Obviously, a special emphasis is given to those articles which were carefully selected for the special issue at hand. These articles demonstrate how mathematical models of different kinds – bottom-up agent models and top-down model approaches – increase the understanding of natural swarm systems, for example, by investigating the efficiency in the self-regulation of social insect colonies. A second group of articles illustrates how mathematical models are used to generate and optimize artificial swarm systems as engineered in the field of swarm robotics. We discuss the common problems of such modelling efforts and highlight the importance of models as generators of hypotheses that suggest novel empiric experiments as well as the importance of empiric experimentation that results in meaningful model parameterizations. In summary, the special issue at hand, which is introduced by this article, gives a significant overview about the lines of research that are followed in the research of swarm systems today.

Using the Swarm Leader Concept for Multi-Agent Obstacle Avoidance

The understanding of the operating principles and stability of natural swarms has recently proven to be a useful tool for the design and control of artificial robotic agents. In previous work [1], an algorithm that successfully enhances the navigation performance of a swarm of robots using the swarm leader concept was introduced. An extension to that work is presented in this paper. The real implantation of some applications mentioned in [1] is presented. The new topic in this paper is to use the simulations along with the experimental implementation using a swarming behaviour test-bed, which has been built from scratch to test the swarming behaviour for multi-agent systems. The main objectives are to design and fabricate a mobile robot test-bed, using lego kits as a basis to test the behaviour of 5-6 interacting mobile robots, and to develop a model to simulate the mobile robots‟ behaviour. These objectives help in designing a series of experiments to explore collective behaviour in swarm robotics with non-complicated way of communication between swarm members. Anarena has been installed to perform a range of experiments with the swarm. Through the use of signalling between the agents, such experiments can be developed to achieve high-level goals such as cooperative search tasks and assembly. Experimental and simulation results match in a way that confirms the efficiency of the algorithm as well as its applicability.

Swarm intelligence: the state of the art special issue of natural computing

It has long been recognised that nature provides many stunning and intriguing examples of group behaviour. Different species of bird swarm in varieties of numbers and in varieties of formations; ants collective hunt food in huge numbers; schools of fish adopt close knit and ever-changing formations as they bewilder predators and bewitch prey. The activities of so-called swarms of organisms in many cases lead to beneficial effects for the organisms in question, and this has led to the concept of swarm intelligence, which encapsulates the idea that the behaviour of a swarm can exhibit useful, functional and intelligent outcomes which seem well beyond the capabilities, as far as we understand, of any individual in the swarm. A common way to define swarm intelligence is as the emergent and collective intelligence of groups of simple and autonomous agents. Implicit in such a definition is the fact that there is assumed to be no central controller—no ‘master’ agent that directs and conducts the activities of its fellows. Each agent is a peer, acting independently, interacting with its brethren, and other features of its environment, according to relatively simple rules. That such a system, with no central controller, can exhibit purposeful and robust behaviour is one of the appealing factors for scientists and engineers, suggesting strategies for building systems that are robust to failures. E.g. when systems rely on a centralised controller, damage to the controller will clearly be disastrous. Swarm intelligence is of great interest to scientists and engineers for two other main reasons. First, scientists need to understand swarms—to understand how the interactions within swarms help social animals achieve their various goals, to understand how swarm behaviour evolved, and so on. Secondly, and of most import to us here, is the fact that the study of natural swarm intelligence leads directly to novel algorithms that have a wide variety of applications. In

Spatial swarm segregation and reproductive isolation between the molecular forms ofAnopheles gambiae

Anopheles gambiae, the major malaria vector in Africa, can be divided into two subgroups based on genetic and ecological criteria. These two subgroups, termed the M and S molecular forms, are believed to be incipient species. Although they display differences in the ecological niches they occupy in the field, they are often sympatric and readily hybridize in the laboratory to produce viable and fertile offspring. Evidence for assortative mating in the field was recently reported, but the underlying mechanisms awaited discovery. We studied swarming behaviour of the molecular forms and investigated the role of swarm segregation in mediating assortative mating. Molecular identification of 1145 males collected from 68 swarms in Donéguébougou, Mali, over 2 years revealed a strict pattern of spatial segregation, resulting in almost exclusively monotypic swarms with respect to molecular form. We found evidence of clustering of swarms composed of individuals of a single molecular form within the village. Tethered M and S females were introduced into natural swarms of the M form to verify the existence of possible mate recognition operating within-swarm. Both M and S females were inseminated regardless of their form under these conditions, suggesting no within-mate recognition. We argue that our results provide evidence that swarm spatial segregation strongly contributes to reproductive isolation between the molecular forms in Mali. However this does not exclude the possibility of additional mate recognition operating across the range distribution of the forms. We discuss the importance of spatial segregation in the context of possible geographic variation in mechanisms of reproductive isolation.

Discrete Particle Swarm Optimization for the minimum labelling Steiner tree problem

Particle Swarm Optimization is a population-based method inspired by the social behaviour of individuals inside swarms in nature. Solutions of the problem are modelled as members of the swarm which fly in the solution space. The improvement of the swarm is obtained from the continuous movement of the particles that constitute the swarm submitted to the effect of inertia and the attraction of the members who lead the swarm. This work focuses on a recent Discrete Particle Swarm Optimization for combinatorial optimization, called Jumping Particle Swarm Optimization. Its effectiveness is illustrated on the minimum labelling Steiner tree problem: given an undirected labelled connected graph, the aim is to find a spanning tree covering a given subset of nodes, whose edges have the smallest number of distinct labels.

Optimal view angle in collective dynamics of self-propelled agents

We study a system of self-propelled agents with the restricted vision. The field of vision of each agent is only a sector of disk bounded by two radii and the included arc. The inclination of these two radii is characterized by the view angle. The consideration of restricted vision is closer to the reality because natural swarms usually do not have a panoramic view. Interestingly, we find that there exists an optimal view angle, leading to the fastest direction consensus. The value of the optimal view angle depends on the density, the interaction radius, the absolute velocity of swarms, and the strength of noise. Our findings may invoke further efforts and attentions to explore the underlying mechanism of the collective motion.

Collective control of spacecraft swarms for space exploration

Swarms are characterized in nature by a dynamic behaviour which is quite appealing for researchers involved in numerous fields of study, like robotics, computer science, pure mathematics and space sciences. Global group organization acquired in absence of centralized control is the feature of natural swarms which is most interesting to reproduce. This study proposes to make use of some evolutionary robotics findings in order to obtain the autonomous group organization in the framework of a deeper knowledge of the astrodynamics. The main task which will be accomplished is the implementation of the control laws for the single satellite. A careful tuning of the parameters at member level is necessary in order to gain an autonomously evolving global behaviour in a number of space missions of immediate interest. In remote sensing missions, for example, trains of a small number of satellites are already orbiting and integrating their collected data: in near future entire swarms of agents could accomplish this task, and should be controlled in order to acquire and maintain the desired leader-follower configuration. Another example can be seen in deep space exploration of unknown celestial bodies, where the migration of the entire swarm from a reference orbit to a (previously unknown) targeted one is an issue; the same group migration is of interest in Earth orbit, when transferring from parking to operational orbit. Finally, self-assembly of rigid-like virtual structures is also simulated. This paper shows that all these cases are autonomously performed by the swarm by correctly implementing four simple rules at individual level, which assess the primal needs for any satellite: avoid collision, remain grouped, align to the neighbor, reach a goal.

Collective robotic search using hybrid techniques: Fuzzy logic and swarm intelligence inspired by nature

This paper presents two new strategies for navigation of a swarm of robots for target/mission focused applications including landmine detection and firefighting. The first method presents an embedded fuzzy logic approach in the particle swarm optimization (PSO) algorithm robots and the second method presents a swarm of fuzzy logic controllers, one on each robot. The framework of both strategies has been inspired by natural swarms such as the school of fish or the flock of birds. In addition to the target search using the above methods, a hierarchy for the coordination of a swarm of robots has been proposed. The robustness of both strategies is evaluated for failures or loss in swarm members. Results are presented with both strategies and comparisons of their performance are carried out against a greedy search algorithm.

Swarm intelligence and signal processing [DSP Exploratory

Swarm intelligence deals with the behavior of natural or artificial swarms. Swarms are systems that consist of many individuals that are organized and coordinated by principles of decentralized control, indirect communication, and self-organization. Examples of natural swarms are social insect colonies, flocks of birds, schools of fish, or herds of land animals. Examples of artificial swarms include groups of robots, intelligent mobile devices that can communicate with each other, or virtual swarms in form of a computer program. An interesting phenomenon of swarms is that collective swarm behavior can emerge on a global scale even when all individuals have only a restricted view of the system and interactions between individuals and their environment occur only on a local scale. Examples for such collective behavior are the nest building of ants and termites or the coordinated movement of a fish school in reaction to the occurrence of prey. In this article we focus on the optimization methods that have been developed in the field of swarm intelligence.

FLAR: AN ADAPTIVE FUZZY ROUTING ALGORITHM FOR COMMUNICATIONS NETWORKS USING MOBILE ANTS

Swarm intelligence, as demonstrated by a natural biological swarm, such as an ant colony, has many powerful properties that are desirable for effective routing in communications networks. In this paper, we propose an intelligent routing algorithm that we are calling Fuzzy Logic Ant-based Routing (FLAR), which is inspired by ant colonies and enhanced by fuzzy logic techniques. Using a fuzzy system as an intelligent and expert mechanism allows multiple constraints to be considered in a simple and intuitive way. Simulation results and a comparison of the proposed method with two state-of-the-art routing algorithms show better performance and a higher fault tolerance for our approach, particularly in regard to link failures.

A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications

Particle Swarm Optimization (PSO), in its present form, has been in existence for roughly a decade, with formative research in related domains (such as social modelling, computer graphics, simulation and animation of natural swarms or flocks) for some years before that; a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridisation and specialisation, and demonstration of some interesting emergent behaviour. This paper aims to offer a compendious and timely review of the field and the challenges and opportunities offered by this welcome addition to the optimization toolbox. Part I discusses the location of PSO within the broader domain of natural computing, considers the development of the algorithm, and refinements introduced to prevent swarm stagnation and tackle dynamic environments. Part II considers current research in hybridisation, combinatorial problems, multicriteria and constrained optimization, and a range of indicative application areas.

A review of particle swarm optimization. Part I: background and development

Particle Swarm Optimization (PSO), in its present form, has been in existence for roughly a decade, with formative research in related domains (such as social modelling, computer graphics, simulation and animation of natural swarms or flocks) for some years before that; a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridisation and specialisation, and demonstration of some interesting emergent behaviour. This paper aims to offer a compendious and timely review of the field and the challenges and opportunities offered by this welcome addition to the optimization toolbox. Part I discusses the location of PSO within the broader domain of natural computing, considers the development of the algorithm, and refinements introduced to prevent swarm stagnation and tackle dynamic environments. Part II considers current research in hybridisation, combinatorial problems, multicriteria and constrained optimization, and a range of indicative application areas.