Swarm Intelligence Algorithms and Their Applications

Author: Shang Gang
Publisher:
Publish Date: 2006-05-01
Features: As an emerging evolutionary computation technique, swarm intelligence algorithms have become a focal point of increasing attention among researchers. They have a very special connection with artificial life, particularly evolutionary strategies and genetic algorithms. The theoretical research field of swarm intelligence mainly includes two algorithms: ant colony algorithms and particle swarm optimization algorithms. The ant colony algorithm simulates the food collection process of ant colonies and has been successfully applied to many discrete optimization problems. The particle swarm optimization algorithm also originated from the simulation of simple social systems; it was initially designed to simulate the foraging process of bird flocks, but later it was found to be an excellent optimization tool. This book systematically describes the theory, implementation techniques, and applications of ant colony algorithms and particle swarm optimization algorithms, and briefly introduces fish swarm algorithms. The book emphasizes the hybridization of various algorithms, discussing the hybridization of ant colony algorithms with simulated annealing algorithms, ant colony algorithms with genetic algorithms, ant colony algorithms with chaos theory, simulated annealing algorithms, genetic algorithms with particle swarm optimization algorithms, chaos theory with particle swarm optimization algorithms, and ant colony algorithms with particle swarm optimization algorithms. The book also discusses the applications of swarm intelligence algorithms in areas such as the traveling salesman problem, weapon-target allocation problems, multiprocessor scheduling problems, reliability optimization problems, clustering problems, and job scheduling problems. This book can serve as a reference for senior undergraduate students, master's and doctoral students in information-related fields, as well as a broad range of scientific and technological workers engaged in intelligent algorithm research. The book systematically describes the theory and implementation techniques of ant colony algorithms and particle swarm optimization algorithms, briefly introduces fish swarm algorithms, emphasizes the hybridization of various algorithms, and discusses the applications of ant colony algorithms with simulated annealing algorithms and genetic algorithms.