Swarm Intelligence: Principles, Advances, and Applications

Swarm Intelligence: Principles, Advances, and Applications delivers in-depth coverage of bat, artificial fish swarm, firefly, cuckoo search, flower pollination, artificial bee colony, wolf search, and gray wolf optimization algorithms. The book begins with a brief introduction to mathematical optimization, addressing basic concepts related to swarm intelligence, such as randomness, random walks, and chaos theory. The text then:

• Describes the various swarm intelligence optimization methods, standardizing the variants, hybridizations, and algorithms whenever possible
• Discusses variants that focus more on binary, discrete, constrained, adaptive, and chaotic versions of the swarm optimizers
• Depicts real-world applications of the individual optimizers, emphasizing variable selection and fitness function design
• Details the similarities, differences, weaknesses, and strengths of each swarm optimization method
• Draws parallels between the operators and searching manners of the different algorithms

Swarm Intelligence: Principles, Advances, and Applications presents a comprehensive treatment of modern swarm intelligence optimization methods, complete with illustrative examples and an extendable MATLAB^® package for feature selection in wrapper mode applied on different data sets with benchmarking using different evaluation criteria. The book provides beginners with a solid foundation of swarm intelligence fundamentals, and offers experts valuable insight into new directions and hybridizations.

《群體智能：原理、進展與應用》深入探討了蝙蝠、人工魚群、螢火蟲、布穀鳥搜尋、花粉傳播、人工蜜蜂群、狼搜尋和灰狼優化算法等群體智能優化方法。本書首先簡要介紹了數學優化的基本概念，包括與群體智能相關的隨機性、隨機漫步和混沌理論。接著，本書：

- 詳細描述了各種群體智能優化方法，並在可能的情況下對變體、混合和算法進行標準化；
- 討論了更多關注二進制、離散、受限、適應性和混沌版本的群體優化器的變體；
- 描繪了各個優化器的實際應用，強調變量選擇和適應函數設計；
- 詳細介紹了每種群體優化方法的相似性、差異性、弱點和優勢；
- 將不同算法的運算子和搜索方式進行了對比。

《群體智能：原理、進展與應用》提供了現代群體智能優化方法的全面介紹，並附有豐富的實例和可擴展的MATLAB套件，用於在不同數據集上應用特徵選擇的包裝模式，並使用不同的評估標準進行基準測試。本書為初學者提供了堅實的群體智能基礎，並為專家提供了有關新方向和混合方法的寶貴見解。