Swarm Intelligence Systems Research Articles

Principles and applications of swarm intelligence for adaptive routing in telecommunications networks

In the past few years, there has been much research on the application of swarm intelligence to the problem of adaptive routing in telecommunications networks. A large number of algorithms have been proposed for different types of networks, including wired networks and wireless ad hoc networks. In this paper, we give an overview of this research area. We address both the principles underlying the research and the practical applications that have been proposed. We start by giving a detailed description of the challenges in this problem domain, and we investigate how swarm intelligence can be used to address them. We identify typical building blocks of swarm intelligence systems and we show how they are used to solve routing problems. Then, we present Ant Colony Routing, a general framework in which most swarm intelligence routing algorithms can be placed. After that, we give an extensive overview of existing algorithms, discussing for each of them their contributions and their relative place in this research area. We conclude with an overview of future research directions that we consider important for the further development of this field.

Overhead reduction in a distributed path management system

CEAS (cross entropy ant system) is a distributed, robust and adaptive swarm intelligence system for path management in communication networks. This paper focuses on strategies for handling the overhead in terms of processing cycles, memory storage, and number of management packets (ants) generated by CEAS when the state of the network changes. Pheromone sharing is introduced such that virtual connections with common sub-paths are sharing information and cooperate in the path finding when the paths have the same destination and the same objective function. The sharing of information reduces the required memory in each node significantly on the expense of an increase in the size of the management packets. However, the packets are still rather small. The cooperation also leads to an improvement in convergence rates which again results in reduced transmission overhead. A rate adjustment scheme is also proposed. The scheme is self-tuned and detects state changes implicitly and sets packet rates accordingly by monitoring parameter values in the management system. Rate adaptation can be done both in the network nodes and at the end-points of a virtual path. Compared to a fixed rate strategy the self-tuned strategies show a significant reduction in the number of packets generated, while maintaining the same data packet delay and service availability level. The self-tuned rate adjustment in the network nodes provides fast restoration with short path detection times, which ensures high service availability. The self-tuned ant rate in the end-points avoids flooding the network with management packets when these are not required. The performance and overhead of CEAS are compared to those of the link state routing currently in use in today's networks. The results show that CEAS outperforms link state routing both with respect to performance and overhead when the network experiences transient link failures, while the opposite is the case with long lived failures.

On model design for simulation of collective intelligence

The study of collective intelligence (CI) systems is increasingly gaining interest in a variety of research and application domains. Those domains range from existing research areas such as computer networks and collective robotics to upcoming areas of agent-based and insect-based computing; also including applications on the internet and in games and movies. CI systems are complex by nature and (1) are effectively adaptive in uncertain and unknown environments, (2) can organise themselves autonomously, and (3) exhibit ‘emergent’ behaviour. Among others, multi-agent systems, complex adaptive systems, swarm intelligence and self-organising systems are considered to be such systems. The explosive wild growth of research studies of CI systems has not yet led to a systematic approach for model design of these kinds of systems. Although there have been recent efforts on the issue of system design (the complete design trajectory from identifying system requirements up to implementation), the problem of choosing and specifying a good model of a CI system is often done implicitly and sometimes even completely ignored. The aim of this article is to bring to the attention that model design is an essential as well as an integral part of system design. We present a constructive approach to systematically design, build and test models of CI systems. Because simulation is often used as a way to research CI systems, we particularly focus on models that can be used for simulation. Additionally, we show that it is not necessary to re-invent the wheel: here, we show how existing models and algorithms can be used for CI model design. The approach is illustrated by means of two example studies on a (semi-automated) multi-player game and collaborative robotics.

Applying adaptive swarm intelligence technology with structuration in web-based collaborative learning

One of the key challenges in the promotion of web-based learning is the development of effective collaborative learning environments. We posit that the structuration process strongly influences the effectiveness of technology used in web-based collaborative learning activities. In this paper, we propose an ant swarm collaborative learning (ASCL) environment based on a swarm intelligence system (SIS) that structures opportunities for effective collaboration and learning in a dynamic way. The results of our experiments indicate that: (1) the self-organizing behavior of SIS is positively associated with system appropriation; (2) the multi-agent-based mechanism of SIS is positively associated with system appropriation; (3) the cohesive capability of SIS is positively associated with system appropriation; and (4) the learner’s tendency toward system appropriation is positively associated with learning effectiveness. Our findings also show that learners in an ASCL environment outperform their counterparts in a general web-based learning (GWL) environment. We conclude that different types of technological support can influence the achievement in a web-based learning environment.

Adaptive Computation Paradigm in Knowledge Representation

The constant demand for complex applications, the ever increasing complexity and size of software systems, and the inherently complicated nature of the information drive the needs for developing radically new approaches for information representation. This drive is leading to creation of new and exciting interdisciplinary fields that investigate convergence of software science and intelligence science, as well as computational sciences and their applications. This survey article discusses the new paradigm of the algorithmic models of intelligence, based on the adaptive hierarchical model of computation, and presents the algorithms and applications utilizing this paradigm in data-intensive, collaborative environment. Examples from the various areas include references to adaptive paradigm in biometric technologies, evolutionary computing, swarm intelligence, robotics, networks, e-learning, knowledge representation and information system design. Special topics related to adaptive models design and geometric computing are also included in the survey.

A distributed learning algorithm for particle systems

Particle systems, a type of swarm intelligence system, have repeatedly been shown to be capable of more general problem solving than just collective movements. The emerging, self-organizing behavior of such systems leads to collective behavior that t

Swarm intelligence systems for transportation engineering: Principles and applications

Agent-based modeling is an approach based on the idea that a system is composed of decentralized individual “agents” and that each agent interacts with other agents according to localized knowledge. Special kinds of artificial agents are the agents created by analogy with social insects. Social insects (bees, wasps, ants, and termites) have lived on Earth for millions of years. Their behavior is primarily characterized by autonomy, distributed functioning, and self-organizing capacities. Social insect colonies teach us that very simple organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. Swarm intelligence is the branch of artificial intelligence based on study of behavior of individuals in various decentralized systems. The paper presents a classification and analysis of the results achieved using swarm intelligence (SI) to model complex traffic and transportation processes. The primary goal of this paper is to acquaint readers with the basic principles of Swarm Intelligence, as well as to indicate potential swarm intelligence applications in traffic and transportation.

Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model environmental systems. We review some of them and their environmental applicability, with examples and a reference list. The techniques covered are case-based reasoning, rule-based systems, artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, swarm intelligence, reinforcement learning and hybrid systems.

SWARM INTELLIGENCE SYSTEMS USING GUIDED SELF-ORGANIZATION FOR COLLECTIVE PROBLEM SOLVING

Drawing inspiration from social interactions in nature, the field of swarm intelligence has presented a promising approach to the design of complex systems consisting of numerous, usually homogeneous, simple parts, to solve a wide variety of problems. Like cellular automata, swarm-intelligence systems involve highly parallel computations across space, based heavily on self-organization, the emergence of global behavior through local interactions of components, and the absence of centralized or global control. However, this has a disadvantage as the desired behavior of a system becomes hard to predict or design based on its local interaction rules. In our ongoing research, we propose to provide greater control over a system, and potentially more useful, goal-oriented behavior, by introducing layered, hierarchical controllers in the particles or components. The layered controllers allow each particle to extend their reactive behavior in a more goal-oriented style, while keeping the locality of the interactions and the general simplicity of the system. In this paper, we present three systems designed using this approach: a competitive foraging system, a system for the collective transport and distribution of goods, and a self-assembly system capable of creating complex structures in a 3D world. Our simulation results show that in all three cases it was possible to guide the self-organization process at different levels of the designated task, suggesting that the self-organizing behavior of swarm-intelligence systems may be extendable to support problem solving in various contexts, such as coordinated robotic teams.

Manufacturing system behaviour using self-organisation

The paper presents a comprehensive simulated case of a manufacturing system's self-organising capability, based on swarm intelligence concepts, intelligent agents, emerging behaviour and system survivability. The work presents and refers to a designed environment including robots, AGVs and respective handled objects, reaching a self-organised status in an unpredictable environment. A concept of rule-based reasoning is applied through agents, facilitating the process of swarm intelligence. The work incorporates aspects of the system's emerging behaviour and its ability to 'survive' from uncertainty to organised status. Simulation of a typical environment is included, and potential extension to a variety of manufacturing environments is discussed.

Simulation of Swarm Intelligence and Possible Applications in Engineering

Modeling biological and natural systems in order to solve complex problems have become popular. Traditional techniques fail at solving some types of problems. On the other hand, it is seen that these kind of problems are solved in nature without help of human. Swarm intelligence(SI) as a research field, proposes such solutions. SI models the collective behavior of the social insects like ants, bees or termites and their coordination without communication. The emerged intelligence has some special characteristics and it can be applied to some areas. Some of the application fields are robotics, networking, finance and entertainment. In this paper, principles of SI systems, original simulations of some SI techniques, and new approaches are presented.