Swarm Intelligence: Why Our World Would End If Swarm Intelligence Disappeared?

By Fouad Sabry

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.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jan 18, 2021
• ISBN: 9781005342104

Book preview

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 Sabry

Visit 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