Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications

Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains.

Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.

《元啟發式演算法手冊：從基礎理論到高級應用》提供了對元啟發式演算法的簡要介紹，從基礎概念到高級解決方案。雖然讀者可能能在網上找到一些元啟發式演算法的原始碼，但編碼風格和解釋通常有很大不同，因此需要擴展理論和實現之間的知識。本書還可以幫助學生和研究人員在計算機科學和應用工程領域的人工智慧研究中構建元啟發式和無監督演算法的綜合視角。

元啟發式演算法可以被視為工程和人工智慧問題優化的無監督學習演算法的典範，包括模擬退火（SA）、禁忌搜索（TS）、遺傳演算法（GA）、螞蟻優化（ACO）、粒子群優化（PSO）、差分進化（DE）等。與大多數需要標記數據進行學習和構建決策模型的監督學習演算法不同，元啟發式演算法繼承了無監督學習演算法的特點，用於解決複雜的工程優化問題，無需標記數據，就像自學習一樣，找到解決複雜問題的方法。