Swarm Intelligence: Collaborative Algorithms and Distributed Problem Solving in Robotics

Chapters Brief Overview:

1: Swarm intelligence: Introduces the concept of collective behavior in decentralized systems, vital for understanding multiagent robotics.

2: Genetic algorithm: Explores evolutionary principles applied to problemsolving, a cornerstone of optimization techniques in robotics.

3: Evolutionary algorithm: Delves into the evolution of algorithms to improve solutions iteratively, crucial for autonomous robotic systems.

4: Swarm behaviour: Investigates how swarm systems operate and collaborate, essential for creating responsive robotic networks.

5: Evolutionary computation: Highlights computational strategies inspired by biological evolution, enhancing robotic adaptability.

6: Particle swarm optimization: Introduces a populationbased method inspired by natural systems, ideal for solving complex optimization problems in robotics.

7: Boids: Discusses flocking algorithms for simulating natural group behaviors, influencing swarm robotics for coordinated movement.

8: Ant colony optimization algorithms: Shows how ants’ foraging behavior provides a framework for solving routing and optimization problems in robotic navigation.

9: Metaheuristic: Explores highlevel problemsolving strategies, expanding robotics capabilities by refining optimization processes.

10: Marco Dorigo: Focuses on the work of Marco Dorigo, pioneering research in swarm intelligence, a key influence in robotics evolution.

11: Computational intelligence: Examines AI's role in robotics, demonstrating how computational techniques empower robots to think and learn autonomously.

12: Stochastic diffusion search: Introduces random search strategies for optimization, an essential tool for autonomous decisionmaking in robotics.

13: Ant robotics: Explores the application of ant colony optimization in robotic systems, emphasizing efficiency in swarm robotics.

14: Firefly algorithm: Unveils the fireflyinspired optimization algorithm, showing its potential in dynamic and realtime robotic control.

15: Metaoptimization: Delves into improving optimization algorithms themselves, crucial for enhancing the performance of robotic systems.

16: Fly algorithm: Focuses on a bioinspired optimization algorithm, expanding the toolkit for solving complex robotic control tasks.

17: Table of metaheuristics: Provides a comprehensive reference to metaheuristic algorithms, a key resource for optimizing robotic systems.

18: Maurice Clerc (mathematician): Highlights the contributions of Maurice Clerc, deepening the understanding of particle swarm optimization’s role in robotics.

19: Atulya Nagar: Focuses on Atulya Nagar's work in computational intelligence, exploring its relevance to robotic decisionmaking and adaptability.

20: Genetic programming: Introduces genetic programming as a way to evolve solutions for robotic systems, paving the way for autonomous development.

21: Neuroevolution of augmenting topologies: Explores how neuroevolution helps optimize neural networks for complex robotic tasks, a cuttingedge area in robotics research.