30 Case Studies on MATLAB Intelligent Algorithms

"MATLAB Intelligent Algorithm 30 Case Analysis" is a summary of the author's years of experience in algorithm research. All cases in the book are carefully designed based on the personal needs of netizens in major domestic MATLAB technology forums. Many of the cases involve content and solution methods that are rarely introduced in MATLAB books published in China. "MATLAB Intelligent Algorithm 30 Case Analysis" adopts the form of cases and takes intelligent algorithms as the main line to explain the most commonly used intelligent algorithms such as genetic algorithm, immune algorithm, annealing algorithm, particle swarm algorithm, fish swarm algorithm, ant colony algorithm and neural network algorithm. MATLAB implementation of the algorithm. "MATLAB Intelligent Algorithm 30 Case Analysis" gives a total of 30 cases. Each case is a specific example of using intelligent algorithms to solve problems. All cases are composed of four parts: theoretical explanation, case background, MATLAB program implementation and extended reading. It is composed of a complete original program, which enables readers to quickly improve their ability to use algorithms to solve practical problems while mastering algorithms. "MATLAB Intelligent Algorithm 30 Case Analysis" can be used as a reference book for undergraduate graduation projects, graduate project design, and junior doctoral project design. It also has high reference value for the majority of scientific researchers.

• Chapter 1：MATLAB Genetic Algorithm Toolbox from the University of Sheffield (1 hours and 45 minutes and 32 seconds)

• Chapter 2：Function optimization algorithm based on genetic algorithm and nonlinear programming (41 minutes and 20 seconds)

• Chapter 3：BP neural network optimization algorithm based on genetic algorithm (45 minutes and 9 seconds)

• Chapter 4：TSP algorithm based on genetic algorithm (47 minutes and 43 seconds)

• Chapter 5：Optimal design of LQR controller based on genetic algorithm (17 minutes and 29 seconds)

• Chapter 6：Detailed explanation and application of genetic algorithm toolbox (31 minutes and 59 seconds)

• Chapter 7：Functional Algorithm of Multiple Population Genetic Algorithms (42 minutes and 44 seconds)

• Chapter 8：Function optimization algorithm based on quantum genetic algorithm (43 minutes and 35 seconds)

• Chapter 9：Multi-objective optimization algorithm based on genetic algorithm (35 minutes and 37 seconds)

• Chapter 10：Multi-objective search algorithm based on particle swarm landing (27 minutes and 10 seconds)

• Chapter 11：Workshop scheduling algorithm based on multi-layer coding genetic algorithm (39 minutes and 26 seconds)

• Chapter 12：Application of immune optimization algorithm in location selection of logistics distribution center (33 minutes and 59 seconds)

• Chapter 13：Particle swarm optimization algorithm (39 minutes and 51 seconds)

• Chapter 14：Optimal design of PID controller based on particle swarm algorithm (26 minutes and 25 seconds)

• Chapter 15：TSP search algorithm based on hybrid particle swarm algorithm (38 minutes and 11 seconds)

• Chapter 16：Dynamic environment optimization algorithm based on dynamic particle swarm optimization (42 minutes and 30 seconds)

• Chapter 17：Function optimization algorithm based on PSO toolbox (33 minutes and 27 seconds)

• Chapter 18：Function optimization algorithm based on fish swarm algorithm (54 minutes and 4 seconds)

• Chapter 19：TSP algorithm based on simulated annealing algorithm (44 minutes and 8 seconds)

• Chapter 20：Clustering algorithm based on genetic simulated annealing algorithm (46 minutes and 38 seconds)

• Chapter 21：Simulated annealing algorithm toolbox and applications (23 minutes and 44 seconds)

• Chapter 22：Optimization calculation of ant colony algorithm - Traveling Salesman Problem (TSP) optimization (40 minutes and 18 seconds)

• Chapter 23：Two-dimensional path planning algorithm based on ant colony algorithm (39 minutes and 17 seconds)

• Chapter 24：Three-dimensional path planning algorithm based on ant colony algorithm (38 minutes and 41 seconds)

• Chapter 25：Regression fitting of neural networks with tutors - prediction of gasoline octane number based on near-infrared spectroscopy (46 minutes and 42 seconds)

• Chapter 26：There is a tutor to learn the classification of neural networks - iris type identification (42 minutes and 6 seconds)

• Chapter 27：Classification of Tutorless Learning Neural Network - Mine Water Inrush Source Identification (39 minutes and 51 seconds)

• Chapter 28：Classification of support vector machines—diagnosis of breast cancer based on electrical impedance characteristics of breast tissue (53 minutes and 54 seconds)

• Chapter 29：Regression fitting of support vector machine - prediction of concrete compressive strength (40 minutes and 10 seconds)

• Chapter 30：Regression fitting and classification of extreme learning machine - comparative experimental study (41 minutes and 31 seconds)