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- Herausgeber: One Billion Knowledgeable
- Kategorie: Wissenschaft und neue Technologien
- Serie: Encyclopedia of Emerging Technologies
- Sprache: Englisch
- Veröffentlichungsjahr: 2021
What Is Swarm Intelligence
- Traders deciding on the next big market bet.
- A navigation app quickly mapping out a less-explored area.
- Fashion brands choosing the hottest color of the season.
- An airport managing fight delays.
What do these scenarios have in common?
In each one, Swarm Intelligence blends global and local insight to improve how businesses make decisions.
Swarm Intelligence is a form of artificial intelligence (AI) inspired by the insect kingdom. In nature, it describes how honeybees migrate, how ants form perfect trails, and how birds flock. In the world of AI, swarm systems draw input from individual people or machine sensors and then use algorithms to optimize the overall performance of the group or system in real time.
Consider Waze, the popular road navigation app that uses Swarm Intelligence to create and modify maps. Starting with limited digital maps, it began making tweaks based on its users’ GPS data along with manual map modifications by registered users. Entire cities have been mapped using this method, as was the case in Costa Rica’s capital, San José. And just as ants signal danger to their counterparts, so too do Waze users contribute live information from accident locations and traffic jams.
Swarm Intelligence is now being used to predict everything from the outcome of the Super Bowl to fashion trends to major political events. Using Swarm Intelligence, investors can better predict market movements, and retailers can more accurately forecast sales.
How You Will Benefit
By the end of reading this book, you will have the answers to the public top 100 questions, queries, issues, doubts, problems and inquiries. Most importantly, you will be able master the discussion about the following topics in Swarm Intelligence, and explore the new ways of thinking about life and business:
01 - Fundamental Concepts: Definition, Systems, Nature
02 - Models of Swarm Behavior: Boids, Self-Propelled Particles
03 - Optimization Problem: Elements, Formulations, and Search Solutions
04 - Meta-Heuristic Nature Inspired Optimization Algorithms Inspired by Swarm Intelligence
05 - Meta-Heuristic and Monkeys Problems: Infinite, Finite, and the difference
06 - Common Algorithmic Characteristics and Comparisons: Ant Colony Optimization, Bee Colony Optimization, Bat Algorithm, Cuckoo Search, Particle Swarm Optimization, Firefly Algorithm, Flower Pollination Algorithm, Swarm Intelligence Application Areas, Travelling Salesman Problem, Telecommunication, Image Processing, Engineering Design, Vehicle Routing
07 - Swarm Intelligence Systems: Taxonomy, Natural vs. Artificial, Scientific vs. Engineering
08 - Examples of Swarm Intelligence Systems: Foraging Behavior of Ants, Clustering by a Swarm of Robots, Exploitation of Collective Behaviors of Animal Societies, Swarm-based Data Analysis
09 - Properties of Swarm Intelligence Systems: Individual, Homogeneous, Interaction, Self-Organized
10 - Studies and Applications of Swarm Intelligence Systems: Clustering Behavior of Ants, Nest Building Behavior of Wasp and Termites, Flocking and Schooling in Birds and Fish, Any Colony Optimization, Particle Swarm Optimization, Swarm-based Network Management, Cooperative Behavior in Swarm of Robots.
11- Swarm Intelligence as a Whole New Way of Thinking About Business: Perspective and Advantages
12 - Swarm Intelligence Foraging for Solutions in Telecommunication, Information Technology, Logistics, Manufacturing.
13 - Advantages of Swarm Intelligence for Organizations: Simple Rules Rule, Raiding New Markets, A swarm of Possibilities.
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One Billion Knowledgeable
Swarm Intelligence
A Brain of Brains
Fouad Sabry
Copyright
Swarm Intelligence Copyright © 2021 by Fouad Sabry. All Rights Reserved.
All rights reserved. No part of this book may be reproduced in any form or by any electronic or mechanical means including information storage and retrieval systems, without permission in writing from the author. The only exception is by a reviewer, who may quote short excerpts in a review.
Cover designed by Fouad Sabry.
This book is a work of fiction. Names, characters, places, and incidents either are products of the author’s imagination or are used fictitiously. Any resemblance to actual persons, living or dead, events, or locales is entirely coincidental.
Fouad SabryVisit my website at
https://www.1bkofficial.org/
Dedication
To the enlightened, the ones who see things differently, and want the world to be better -- they are not fond of the status quo or the existing state ... You can disagree with them too much, and you can argue with them even more, but you cannot ignore them, and you cannot underestimate them, because they always change things... they push the human race forward, and while some may see them as the crazy ones or amateur, others see genius and innovators, because the ones who are enlightened enough to think that they can change the world, are the ones who do, and lead the people to the enlightenment.
Epigraph
“The long-term value of amplifying the intelligence of people is a lot more important than betting on sport.” ― Louis B. Rosenberg, CEO of Unanimous AI
Table of CONTENTS
Swarm Intelligence
Copyright
Dedication
Epigraph
Table of CONTENTS
Chapter One: Introduction
Models of Swarm Behavior
Boids
Self-Propelled Particles
Chapter Two: Optimization Problem
Problem Formulation
Solution Search
Chapter Three: Meta-Heuristic Nature-Inspired Optimization Algorithms
Innovative Approaches to Optimization
Ingenious Methods to Solve Difficult Problems
Fundamental Concepts
Chapter Four: Meta heuristics and monkeys
Searches in A Vast Design Space
Infinite Monkey Theorem
Finite Monkey Theorem
Defining Differences
Chapter Five: Common Algorithmic Characteristics
Exploration and Exploitation
Comparison with Other Algorithms
Chapter Six: Algorithm Types
Ant colony optimization
Bee Colony Optimization
Bat Algorithm
Cuckoo Search
Particle Swarm Optimization
Firefly Algorithm
Flower Pollination Algorithm
Chapter Seven: Application Areas of nature-inspired algorithms
Hard Problems
Telecommunications
Image Processing
Engineering Design
Vehicle Routing
Chapter Eight: Future of nature-inspired algorithms
Algorithms Go Beyond Mimicking
Hybrid Algorithms
Self-Adapted and Intelligent Algorithms
Mixture of Modern and Conventional Algorithms
Chapter Nine: Taxonomy of Swarm Intelligence Systems
A Distinct Multidisciplinary Character
Natural vs. Artificial
Scientific vs. Engineering
Chapter Ten: Objectives of Swarm Intelligence Systems
Natural/Scientific: Foraging Behavior of Ants
Artificial/Scientific: Clustering by a Swarm of Robots
Natural/Engineering: Exploitation of collective behaviors of animal societies
Artificial/Engineering: Swarm-based Data Analysis
Chapter Eleven: Properties of a Swarm Intelligence System
The Characteristics
Many Individuals
Homogeneous Individuals
Simple Behavioral Interactions
Self-Organized Group Behavior
The Collective Behavior
Scalable, Parallel and Fault-Tolerant Systems
Scalability
Parallel
Fault Tolerance
Chapter Twelve: Studies of Swarm Intelligence
Clustering Behavior of Ants
Nest Building Behavior of Wasps and Termites
Flocking and Schooling in Birds and Fish
Ant Colony Optimization
Particle Swarm Optimization
Swarm-based Network Management
Cooperative Behavior in Swarms of Robots
Chapter Thirteen: A Whole New Way to Think About Business
Aviation
Manufacturing
Swarm Intelligence from Business Perspective
Flexibility
Robustness
Self-Organization
Chapter Fourteen: Foraging for Solutions
Telecommunications
Information Technology
Logistics
Manufacturing
Chapter Fifteen: The Task of Dividing Tasks
Job Allocation
The Bucket Brigade
Bartholdi and Eisenstein Rule
Chapter Sixteen: The Advantages of Swarm Intelligence
Advantages
Flexibility:
Robustness:
Self-Organization
Simple Rules Rule
Swarm Intelligence Case Study in Information Technology
Chapter Seventeen: Simulation Modelling for Predicting Collective BEHAVIOUR
Dinner Party Mingling
Lesson Learnt
Computer Model for Analyzing Crowd Actions
Examples of Swarm Intelligent Organizations
Chapter Eighteen: Raiding New Markets
Mass
Tandem
Community
Chapter Nineteen: The success and failure in New Markets
Examples of Community Recruitment
Examples of Mass Recruitment
The Nurturing Atmosphere for Idea Market
Chapter Twenty: A Swarm of Possibilities
Possible applications of swarm intelligence can be constrained only by imagination.
Reconfigurable Robot Swarms
Swarm Intelligence Obstacles
New Paradigm
One Billion Knowledgeable
About The AUTHOR
Bio
Where to Find the Author Online
Other books from the same author
Chapter One: Introduction
Swarm intelligence (SI) is the collective behaviour of decentralized, self-organized systems, natural or artificial.
T
he definition of Swarm Intelligence (SI) is used in artificial intelligence work. The term was invented by Gerardo Beni and Jing Wang in 1989 in the sense of robotic cellular systems.
SI systems usually consist of a population of simple agents or bodies communicating locally with each other and their surroundings. Inspiration also comes from nature, particularly from biological systems. Agents obey quite simple rules, and while there is no centralized control system that determines how individual agents should behave, local and, to some degree, random interactions between such agents contribute to the emergence of "intelligent" global activity, unknown to individual agents. Examples of swarm intelligence in natural systems include ant colonies, bird flocking, hawk hunting, animal herding, bacterial growth, fish breeding and microbial intelligence.
Applying swarm concepts to robots is called swarm robotics, while swarm intelligence refers to a more general group of algorithms. Swarm prediction was used in the sense of forecasting issues. Similar approaches to those suggested for swarm robotics are considered in synthetic collective intelligence for genetically modified organisms.
Models of Swarm Behavior
Boids
Boids is an artificial life software created by Craig Reynolds in 1986 that simulates the flocking behavior of birds. His paper on this subject was published in 1987. The word "boid" corresponds to the shortened version of the "bird-oid object" that refers to a bird-like object.
As with most artificial life simulations, Boids is an example of evolving behavior; that is, the complexity of Boids emerges from the interaction of individual agents (in this case the Boids) adhering to a set of basic laws. The laws applied in the simplest world of Boids are as follows:
Separation: steering to prevent the crowding of nearby flock mates.
Alignment: steering towards the average location of the local flock mates.
Cohesion: steering to shift into the average location (center of mass) of the local flock mates.
More specific guidelines, such as the prevention of obstacles and goal seeking, can be introduced.
Self-Propelled Particles
The Self-Propelled Particle (SPP), also referred to as the Vicsek model, was introduced in 1995 by Vicsek et al. as a special case of the Boids model introduced by Reynolds in 1986. A swarm is modeled in SPP by a set of particles that travel at a constant speed but respond to a random disturbance by each time maintaining the average direction of motion of the other particles in their local neighborhoods.
SPP models assume that swarming animals share similar traits at the community level, regardless of the type of animals in the swarm. Swarming processes give rise to evolving behaviors that occur on several different scales, some of which turn out to be both universal and robust. In theoretical physics, it has become a challenge to find minimal statistical models that capture these behaviors.
***
Chapter Two: Optimization Problem
A typical optimization problem has a key design goal, such as cost or energy efficiency.
A
typical optimization problem has a key design goal, such as cost or energy efficiency, but is typically subject to several design constraints that require two optimization phases: the problem formulation to identify and prioritize constraints, and the solution quest to solve the problem with the optimal approach.
Problem Formulation
To demonstrate the issue of optimization, imagine searching for a lost black box on a trans-Atlantic flight. The design constraints of the search—budget, ocean currents, weather, and time—will be contradictory. Weather can cause delays, which may raise costs and operate against the objective of locating a box within a certain timeframe. There is also a strict period limit since the battery of the box has a relatively short life. As a consequence, the problem formulation must consider some trade-offs between cost, time, and likelihood of locating a box, and these trade-offs are subject to changes in various other variables, such as resource availability and location accessibility.
