Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, it focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. The characterizing property of a swarm intelligence system i...

This paper introduces Swarm AI, the first general framework for designing Swarm Intelligence approaches to problems. We outline principles of Swarm AI, discuss its connection to previous work, and analyze the advantages and disadvantages of this method. Finally, we describe a case study of applying Swarm AI to a new problem. Specifically, we discuss the design of a soccer team, using the princi...

This paper presents the design, implementation and experiences of a new three hour experimental course taught for a joint undergraduate and graduate class at the University of Missouri-Rolla, USA. The course covers the four main paradigms of Computational Intelligence (CI) and their integration to develop hybrid systems. The paradigms covered are artificial neural networks (ANNs), evolutionary ...

This chapter considers the application of swarm intelligence principles to collective robotics. Our aim is to identify the reasons for the growing interest in the intersection of these two areas, and to evaluate the progress that has been made to date. In the course of this chapter, we will discuss the implications of taking a swarm intelligent approach, and review recent research and applicati...

In the proposed work, authors have applied swarm intelligence based algorithms for the effective Co-ordination of Flexible AC transmission system (FACTS) devices with other existing Var sources present in the network. IEEE 30 and IEEE 57 bus systems are taken as standard test systems. SPSO (Simple Particle Swarm Optimization) and other two swarm based intelligence approaches like APSO (Adaptive...

This paper presents a swarm intelligence based approach for optimal scheduling in sensor networks. Sensors are characterized by their transaction times and interdependencies. In the presence of interdependencies the problem of optimal scheduling to minimize the overall transaction/response time is modeled as a graph partitioning problem. Graph partitioning problem is a well known NPcomplete pro...

Swarm Intelligence algorithms have proved to be very effective in solving problems on many aspects of Artificial Intelligence. This paper constitutes a first study of the recently proposed Unified Particle Swarm Optimization algorithm on scheduling problems. More specifically, the Single Machine Total Weighted Tardiness problem is considered, and tackled through a scheme that combines Unified P...

This paper discusses how concepts derived from nature can be applied successfully to improve the performance of the rule mining process. These concepts are derived from swarm intelligence and behavior of frogs. Swarm Intelligence (SI) is the property of a system whereby the collective behavior of agents interacting locally with their environment causes coherent functional global patterns to eme...

Internet knowledgebases are more and more described using ontological vocabularies. However, efficient coherent solutions for both federated storage and reasoning upon widely distributed web repositories are rarely explored. We present a self-organized, distributed storage and reasoning approach based on swarm intelligence exploiting the strong applicability of swarm algorithms to distributed e...