Название: Applied Optimization and Swarm Intelligence
Автор: Eneko Osaba, Xin-She Yang
Издательство: Springer
Год: 2021
Страниц: 236
Язык: английский
Формат: pdf (true), epub
Размер: 27.4 MB

This book gravitates on the prominent theories and recent developments of swarm intelligence methods, and their application in both synthetic and real-world optimization problems. The special interest will be placed in those algorithmic variants where biological processes observed in nature have underpinned the core operators underlying their search mechanisms. In other words, the book centers its attention on swarm intelligence and nature-inspired methods for efficient optimization and problem solving. The content of this book unleashes a great opportunity for researchers, lecturers and practitioners interested in swarm intelligence, optimization problems and Artificial Intelligence (AI).

Swarm Intelligence (SI) has arisen as one of the most studied areas within the wider artificial intelligence field. In fact, SI is the most high-growing branch on the current bio-inspired computation community. Most renowned scientific databases support this affirmation, showing a clear crescendo trend in the number of works published around this scientific topic in last years. In a nutshell, SI can be defined as a specific stream of bio-inspired computation, based on the collective intelligence inherent to large populations of agents with simple behavioral patterns of interaction and communication. Arguably, the principal inspirations behind the first conception and subsequent establishment of SI are the well-known Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). These both algorithms trigger the success of SI, being the basis and main influence for the research carried out thereafter.

The following popular nature-inspired algorithms are included in path planning approaches: Particle Swarm Optimization (PSO), genetic algorithms, Gravitational Search Algorithms (GSAs), simulated annealing, chemical optimization, firefly optimization, Charged System Search (CSS), Ant Colony Optimization algorithms, Bee Colony Optimization, Grey Wolf Optimizer (GWO) including the challenging path planning approaches for underwater autonomous vehicles, and Differential Evolution (DE). Hybrid algorithms are also applied successfully in path planning problems as, for example, PSO-GSA, neural networks and fuzzy logic, fuzzy cognitive maps and evolutionary algorithms, DE and Reinforcement Learning (RL) that solve the specific wall following control problem, embedding animal motion attributes and deep RL.