— 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.
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.