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Swarm Intelligence (SI) Models. Swarm intelligence models are referred to as computational models inspired by natural swarm systems. To date, several swarm intelligence models based on different natural swarm systems have been proposed in the literature, and successfully applied in many real-life applications. Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization.[1] Swarm intelligence is the emergent collective intelligence of groups of simple autonomous agents. Swarm Intelligence is a field of computer science that designs and studies efficient computational methods for solving problems in a way that is inspired by the behavior of real swarms or insect colonies (see e.g. Bonabeau et al., ; Kennedy et al., ). Principles of self-organization and local or indirect communication are important for un-. Computational swarm intelligence is modelled on the social behavior of animals and its prin- ciple application is as an optimization technique. Swarm robotics is a relatively new and rapidly developing field which draws inspiration from swarm intelligence. ants, and flocks of birds, the backbone of swarm intelligence research is built mainly upon two families of algorithms: ant colony optimisation, and particle swarm optimisation. Swarm Intelligence Applications in Electric Machines 15 sdr r e ds e M i 1 = (19) * qs 1 e r r sdr e r i M = (20) Model with the Losses of two asymmetrical windings induction motor Finding the losses expression for the two asymmetrical windings induction motor with losses model is a very complex. In this section, a simplified induction motor model with. Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. The inspiration often Cited by: Swarm Intelligence Introduction. cooperation and division of labour adaptive task allocation work stimulation by cultivation pheromones. Ants. ants solve complex tasks by simple local means ant productivity is better than the sum of their single activities ants . Swarm Intelligence (SI) is a relatively new and potentially promising branch of Artificial Intelligence that is used to model the collective intelligent behavior of social swarms in nature. Swarm robotics is a novel approach to the coordination of large numbers of robots and has emerged as the application of swarm intelligence to multi-robot systems. some others who use the term. For example, in Swarm Intelligence: From Natural to Artificial Systems, by Bonabeau, Dorigo, and Theraulaz (), which focuses on the modeling of social insect (primarily ant) behavior, page 7 states: It is, however, fair to say that very few applications of swarm intelli-gence have been developed. Theory and New Applications of Swarm Intelligence. Edited by: Rafael Parpinelli and Heitor S. Lopes. ISBN , PDF ISBN , Published Cited by: 9. Swarm intelligence. Yichen Hu. Abstract Swarm intelligence is an important concept in arti cial intelligence and com- puter science with emergent properties. The essential idea of swarm intelligence algorithms is to employ many simple agents applying almost no rule which in . 1 Swarm Intelligence: Foundations, Perspectives and Applications food. However, as time passes, the pheromone starts to evaporate. The more time it takes for an ant to travel down the path and back again, the more time the pheromone has to evaporate (and the path to become less prominent). Swarm Intelligence in Optimization. Keywords Particle Swarm Optimization Particle Swarm Pareto Front Evolutionary Computation Multiobjective Optimization These keywords were added by machine and not by the authors. This process is experimental and the keywords may Cited by: Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. Swarm intelligence market expected to grow at significant rate between 20The key factors driving the growth of the swarm intelligence market are the increasing usage of swarm intelligence for solving big data problems, the rising adoption of swarm-based drones in the military, and need for swarm intelligence in the transportation business. Swarm Intelligence-based techniques can be used in a number of applications. The U.S. military is investigating swarm techniques for controlling unmanned vehicles. The European Space Agency is thinking about an orbital swarm for self-assembly and interferometry. Swarm and Evolutionary Computation is committed to timely publication of very high-quality, peer-reviewed, original articles that advance the state-of-the art of all aspects of evolutionary computation and swarm intelligence. Survey papers reviewing the state-of-the-art of timely topics will also be welcomed as well as novel and interesting. Swarm Intelligence is the principal peer reviewed publication dedicated to reporting research and new developments in this multidisciplinary field. The journal publishes original research articles and occasional reviews on theoretical, experimental, and practical aspects of swarm intelligence.