Particle Swarm Optimisation: Classical and Quantum Perspectives
暫譯: 粒子群優化：經典與量子視角

Sun, Jun, Lai, Choi-Hong, Wu, Xiao-Jun

Particle Swarm Optimisation: Classical and Quantum Perspectives

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商品描述

Although the particle swarm optimisation (PSO) algorithm requires relatively few parameters and is computationally simple and easy to implement, it is not a globally convergent algorithm. In Particle Swarm Optimisation: Classical and Quantum Perspectives, the authors introduce their concept of quantum-behaved particles inspired by quantum mechanics, which leads to the quantum-behaved particle swarm optimisation (QPSO) algorithm. This globally convergent algorithm has fewer parameters, a faster convergence rate, and stronger searchability for complex problems.

 

 

 

 

 

 

 

The book presents the concepts of optimisation problems as well as random search methods for optimisation before discussing the principles of the PSO algorithm. Examples illustrate how the PSO algorithm solves optimisation problems. The authors also analyse the reasons behind the shortcomings of the PSO algorithm.

 

 

 

 

 

 

 

 

 

Moving on to the QPSO algorithm, the authors give a thorough overview of the literature on QPSO, describe the fundamental model for the QPSO algorithm, and explore applications of the algorithm to solve typical optimisation problems. They also discuss some advanced theoretical topics, including the behaviour of individual particles, global convergence, computational complexity, convergence rate, and parameter selection. The text closes with coverage of several real-world applications, including inverse problems, optimal design of digital filters, economic dispatch problems, biological multiple sequence alignment, and image processing. MATLAB(R), Fortran, and C++ source codes for the main algorithms are provided on an accompanying CD-ROM.

 

 

 

 

 

 

 

 

 

Helping you numerically solve optimisation problems, this book focuses on the fundamental principles and applications of PSO and QPSO algorithms. It not only explains how to use the algorithms, but also covers advanced topics that establish the groundwork for understanding state-of-the-art

 

 

商品描述(中文翻譯)

雖然粒子群優化（Particle Swarm Optimisation, PSO）演算法需要的參數相對較少，且計算上簡單易於實現，但它並不是一個全局收斂的演算法。在《粒子群優化：經典與量子視角》中，作者介紹了受量子力學啟發的量子行為粒子的概念，這導致了量子行為粒子群優化（Quantum-behaved Particle Swarm Optimisation, QPSO）演算法的提出。這個全局收斂的演算法具有更少的參數、更快的收斂速度，以及對於複雜問題更強的搜尋能力。

本書介紹了優化問題的概念以及隨機搜尋方法，然後討論了PSO演算法的原理。範例說明了PSO演算法如何解決優化問題。作者還分析了PSO演算法缺陷背後的原因。

接下來，作者對QPSO演算法的文獻進行了全面的概述，描述了QPSO演算法的基本模型，並探討了該演算法在解決典型優化問題中的應用。他們還討論了一些進階的理論主題，包括個體粒子的行為、全局收斂、計算複雜度、收斂速度和參數選擇。文本最後涵蓋了幾個實際應用，包括逆問題、數位濾波器的最佳設計、經濟調度問題、生物多序列比對和影像處理。主要演算法的MATLAB(R)、Fortran和C++源代碼隨附於CD-ROM中。

本書幫助您數值解決優化問題，重點在於PSO和QPSO演算法的基本原則和應用。它不僅解釋了如何使用這些演算法，還涵蓋了建立理解最先進技術基礎的進階主題。

作者簡介

Jun Sun is an associate professor in the Department of Computer Science and Technology at Jiangnan University. He is also a researcher at the Key Laboratory of Advanced Process Control for Light Industry in China. He has a Ph.D. in control theory and control engineering. His research interests include computational intelligence, numerical optimisation, and machine learning.

 

 

 

 

 

 

 

Choi-Hong Lai is a professor of numerical mathematics in the Department of Mathematical Sciences at the University of Greenwich. He has a Ph.D. in computational aerodynamics and PDEs. His research interests include numerical PDEs, numerical algorithms, and parallel algorithms for industrial applications, such as aeroacoustics, inverse problems, computational finance, and image processing.

 

 

 

 

 

 

 

 

 

Xiao-Jun Wu is a professor at Jiangnan University. He has a Ph.D. in pattern recognition and intelligent systems. He has published more than 150 papers on pattern recognition, computer vision, fuzzy systems, neural networks, and intelligent systems.

 

 

作者簡介(中文翻譯)

Jun Sun 是江南大學計算機科學與技術系的副教授。他同時也是中國輕工業先進過程控制重點實驗室的研究員。他擁有控制理論與控制工程的博士學位。他的研究興趣包括計算智能、數值優化和機器學習。

Choi-Hong Lai 是格林威治大學數學科學系的數值數學教授。他擁有計算氣動力學和偏微分方程（PDEs）的博士學位。他的研究興趣包括數值PDEs、數值算法以及用於工業應用的並行算法，如氣動聲學、逆問題、計算金融和圖像處理。

Xiao-Jun Wu 是江南大學的教授。他擁有模式識別與智能系統的博士學位。他在模式識別、計算機視覺、模糊系統、神經網絡和智能系統方面發表了超過150篇論文。

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