Nature-Inspired Algorithms: For Engineers and Scientists

This comprehensive reference text discusses nature inspired algorithms and their applications. It presents the methodology to write new algorithms with the help of MATLAB programs and instructions for better understanding of concepts. It covers well-known algorithms including evolutionary algorithms, genetic algorithm, particle Swarm optimization and differential evolution, and recent approached including gray wolf optimization. A separate chapter discusses test case generation using techniques such as particle swarm optimization, genetic algorithm, and differential evolution algorithm.

The book-

• Discusses in detail various nature inspired algorithms and their applications
• Provides MATLAB programs for the corresponding algorithm
• Presents methodology to write new algorithms
• Examines well-known algorithms like the genetic algorithm, particle swarm optimization and differential evolution, and recent approaches like gray wolf optimization.
• Provides conceptual linking of algorithms with theoretical concepts

The text will be useful for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering.

Discussing nature inspired algorithms and their applications in a single volume, this text will be useful as a reference text for graduate students in the field of electrical engineering, electronics engineering, computer science and engineering. It discusses important algorithms including deterministic algorithms, randomized algorithms, evolutionary algorithms, particle swarm optimization, big bang big crunch (BB-BC) algorithm, genetic algorithm and grey wolf optimization algorithm.

這本全面的參考書討論了自然啟發演算法及其應用。它介紹了使用MATLAB程式和指導來撰寫新演算法的方法，以幫助讀者更好地理解概念。書中涵蓋了眾多著名的演算法，包括進化演算法、遺傳演算法、粒子群優化和差分進化，以及最近的灰狼優化方法。另外，還有一章專門討論使用粒子群優化、遺傳演算法和差分進化演算法等技術進行測試案例生成的方法。

這本書具體內容包括：
- 詳細討論各種自然啟發演算法及其應用
- 提供相應演算法的MATLAB程式
- 提供撰寫新演算法的方法
- 檢視遺傳演算法、粒子群優化和差分進化等著名演算法，以及灰狼優化等最新方法
- 將演算法與理論概念進行概念連結

這本書對電機工程、電子工程、計算機科學和工程領域的研究生非常有用。

這本書將自然啟發演算法及其應用集中在一個單一的卷中，對電機工程、電子工程、計算機科學和工程領域的研究生來說，它將作為一本參考書非常有用。它討論了重要的演算法，包括確定性演算法、隨機演算法、進化演算法、粒子群優化、大爆炸大壓縮（BB-BC）演算法、遺傳演算法和灰狼優化演算法。

K. K. Mishra is presently working as an assistant professor, department of computer science and engineering, Motilal Nehru National Institute of Technology Allahabad, India. His research areas include genetic algorithm, analysis of algorithm, automata theory, microprocessor and multi-objective optimization. He has taught courses including computer architecture, data structures, advanced computer architecture, programming in C++, microprocessor and automata theory at undergraduate and graduate level. He is a regular reviewer of the Journal of Supercomputing (Springer), Applied Intelligence, Applied Soft Computing, IEEE Transaction on Cybernetics, IEEE System Journal, Neural computing and application, and IETE journals.

K. K. Mishra目前在印度的Motilal Nehru National Institute of Technology Allahabad擔任計算機科學與工程系的助理教授。他的研究領域包括遺傳算法、算法分析、自動機理論、微處理器和多目標優化。他曾在本科和研究生課程中教授計算機架構、數據結構、高級計算機架構、C++編程、微處理器和自動機理論等課程。他是Journal of Supercomputing (Springer)、Applied Intelligence、Applied Soft Computing、IEEE Transaction on Cybernetics、IEEE System Journal、Neural computing and application以及IETE journals的定期審稿人。