Swarm Intelligence Algorithms: Modifications and Applications

Nature-based algorithms play an important role among artificial intelligence algorithms. Among them are global optimization algorithms called swarm intelligence algorithms. These algorithms that use the behavior of simple agents and various ways of cooperation between them, are used to solve specific problems that are defined by the so-called objective function. Swarm intelligence algorithms are inspired by the social behavior of various animal species, e.g. ant colonies, bird flocks, bee swarms, schools of fish, etc. The family of these algorithms is very large and additionally includes various types of modifications to enable swarm intelligence algorithms to solve problems dealing with areas other than those for which they were originally developed.

This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. It contains a short description along with a pseudo-code showing the various stages of its operation. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem.

This book should also be useful for undergraduate and postgraduate students studying nature-based optimization algorithms, and can be a helpful tool for learning these algorithms, along with their modifications and practical applications. In addition, it can be a useful source of knowledge for scientists working in the field of artificial intelligence, as well as for engineers interested in using this type of algorithms in their work.

If the reader wishes to expand his knowledge beyond the basics of swarm intelligence algorithms presented in this book and is interested in more detailed information, we recommend the book Swarm Intelligence Algorithms: A Tutorial (Edited by A. Slowik, CRC Press, 2020). It contains a detailed explanation of how each algorithm works, along with relevant program codes in Matlab and the C ++ programming language, as well as numerical examples illustrating step-by-step how individual algorithms work.

自然基礎演算法在人工智慧演算法中扮演著重要角色，其中包括稱為群體智能演算法的全域最佳化演算法。這些演算法利用簡單代理者的行為和彼此之間的合作方式，用於解決由所謂的目標函數定義的特定問題。群體智能演算法受到各種動物物種的社會行為啟發，例如螞蟻群、鳥群、蜜蜂群、魚群等。這些演算法的家族非常龐大，還包括各種修改型，以使群體智能演算法能夠解決與其最初開發的問題領域不同的問題。

本書介紹了24種群體演算法及其修改型和實際應用。每個章節都專門介紹一種演算法，包括簡短的描述和顯示其操作各個階段的偽代碼。此外，每個章節還包含對演算法的選定修改的描述，並展示如何使用它來解決選定的實際問題。

本書對於攻讀自然基礎最佳化演算法的本科生和研究生也很有用，可以作為學習這些演算法及其修改型和實際應用的有用工具。此外，對於從事人工智慧領域研究的科學家以及對使用這類演算法感興趣的工程師來說，本書也是一個有用的知識來源。

如果讀者希望在本書介紹的群體智能演算法基礎之上擴展知識，並對更詳細的資訊感興趣，我們推薦閱讀《群體智能演算法：教程》（A. Slowik編著，CRC Press，2020）。該書詳細解釋了每個演算法的運作原理，並提供了Matlab和C ++編程語言的相關程式碼，以及逐步說明個別演算法運作的數值示例。

Adam Slowik (IEEE Member 2007; IEEE Senior Member 2012) is an Associate Professor in the Department of Electronics and Computer Science, Koszalin University of Technology. His research interests include soft computing, computational intelligence, and, particularly, bio-inspired optimization algorithms and their engineering applications. He was a recipient of one Best Paper Award (IEEE Conference on Human System Interaction - HSI 2008).

Adam Slowik（2007年IEEE會員，2012年IEEE高級會員）是科茲林科技大學電子與計算機科學系的副教授。他的研究興趣包括軟計算、計算智能，尤其是生物啟發的優化算法及其工程應用。他曾獲得一項最佳論文獎（IEEE人機系統互動會議 - HSI 2008）。