Swarm Intelligent Systems

1. Front Matter

   Pages I-XX

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2. Methodologies Based on Particle Swarm Intelligence

   1. Front Matter

      Pages 1-1

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   2. Swarm Intelligence: Foundations, Perspectives and Applications

      • Ajith Abraham, He Guo, Hongbo Liu

      Pages 3-25

   3. Waves of Swarm Particles (WoSP)

      • Tim Hendtlass

      Pages 27-58

   4. Grammatical Swarm: A Variable-Length Particle Swarm Algorithm

      • Michael O'Neill, Finbar Leahy, Anthony Brabazon

      Pages 59-74

   5. SWARMs of Self-Organizing Polymorphic Agents

      • Derek Messie, Jae C. Oh

      Pages 75-90

3. Experiences Using Particle Swarm Intelligence

   1. Front Matter

      Pages 92-92

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   2. Swarm Intelligence — Searchers, Cleaners and Hunters

      • Yaniv Altshuler, Vladimir Yanovsky, Israel A. Wagner, Alfred M. Bruckstein

      Pages 93-132

   3. Ant Colony Optimisation for Fast Modular Exponentiation using the Sliding Window Method

      • Nadia Nedjah, Luiza de Macedo Mourelle

      Pages 133-147

   4. Particle Swarm for Fuzzy Models Identification

      • Arun Khosla, Shakti Kumar, K. K. Aggarwal, Jagatpreet Singh

      Pages 149-173

   5. A Matlab Implementation of Swarm Intelligence based Methodology for Identification of Optimized Fuzzy Models

      • Arun Khosla, Shakti Kumar, K. K. Aggarwal, Jagatpreet Singh

      Pages 175-184

4. Back Matter

   Pages 185-185

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Swarm intelligence is an innovative computational way to solving hard pr- lems. This discipline is inspired by the behavior of social insects such as ?sh schools and bird ?ocks and colonies of ants, termites, bees and wasps. In g- eral, this is done by mimicking the behavior of the biological creatures within their swarms and colonies. Particle swarm optimization, also commonly known as PSO, mimics the behaviorofaswarmofinsectsoraschoolof?sh.Ifoneoftheparticlediscovers a good path to food the rest of the swarm will be able to follow instantly even if they are far away in the swarm. Swarm behavior is modeled by particles in multidimensionalspacethathavetwocharacteristics:apositionandavelocity. Theseparticleswanderaroundthehyperspaceandrememberthebestposition that they have discovered. They communicate good positions to each other and adjust their own position and velocity based on these good positions. The ant colony optimization, commonly known as ACO, is a probabilistic technique for solving computational hard problems which can be reduced to ?ndingoptimalpaths.ACOisinspiredbythebehaviorofantsin?ndingshort paths from the colony nest to the food place. Ants have small brains and bad vision yet they use great search strategy. Initially, real ants wander randomly to ?nd food. They return to their colony while laying down pheromone trails. If other ants ?nd such a path, they are likely to follow the trail with some pheromone and deposit more pheromone if they eventually ?nd food.

• MATLAB
• agents
• algorithm
• algorithms
• behavior
• computational intelligence
• fuzzy
• intelligence
• intelligent systems
• multi-agent system
• multi-agent systems
• optimization
• particle swarm
• particle swarm algorithm
• swarm intelligence

• Department of Electronics Engineering and Telecommunications - DETEL, Faculty of Engineering - FEN, State University of Rio de Janeiro - UERJ, Maracanã, Brazil

  Nadia Nedjah

• Department of Electronics Engineering and Telecommunications - DESC, Faculty of Engineering - FEN, State University of Rio de Janeiro - UERJ, Maracanã, Brazil

  Luiza de Macedo Mourelle

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