Giải thuật di truyền GA (Genetic Algorithms)

Giải thuật di truyền GA (Genetic Algorithms) GA bắt đầu với một tập hợp các giải pháp (được mã hóa bởi các nhiễm sắc thể (NST)) gọi là quần thể. Quần thể ban đầu thường được thành lập một cách ngẫu nhiên. Các giải pháp từ một quần thể được lấy và sử dụng để tạo ra quần thể mới. Điều này dẫn đến việc các quần thể mới sẽ tốt hơn quần thể cũ. Những giải pháp được lựa chọn theo độ thích nghi sẽ tạo nên các giải pháp mới (con cái) càng phù hợp hơn với hàm số thích nghi thì càng có nhiều cơ hội hơn để tiếp tục tái sinh. Việc lặp sẽ tiếp tục được thực hiện cho đến khi thỏa mãn một hoặc một số điều kiện dừng do chúng ta đặt ra. Các bước chính của giải thuật di truyền được mô tả vắn tắt như sau: 1. [Khởi đầu] Tạo ngẫu nhiên quần thể của N nhiễm sắc thể. 2. [Độ thích nghi] Tính độ thích nghi cho mỗi nhiễm sắc thể x thuộc quần thể: 3. [Quần thể mới] Tạo quần thể mới bằng cách lặp lại các bước cho tới khi quần thể mới được hoàn thành 1. [Chọn lọc] Chọn cặp nhiễm sắc thể bố mẹ từ quần thể dựa trên độ thích nghi của chúng (độ thích nghi càng cao, cơ hội được lựa chọn càng lớn) 2. [Lai] Với một sác xuất lai, lai cặp nhiễm sắc thể bố mẹ để cho ra nhiễm sắc thể con. Nếu việc lai không được thực hiện, nhiễm sắc thể con là bản sao chính xác của bố mẹ. 3. [Đột biến] Với một sác xuất đột biến, biến đổi nhiễm sắc thể con tại một vài vị trí của nhiễm sắc thể được chúng ta lựa chọn. 4. [Thay thế] Sử dụng quần thể mới tạo để tiếp tục chạy chương trình. 5. [Kiếm tra] Nếu điều kiện kết thúc được thỏa mãn, dừng lặp và trả về giải pháp tốt nhất từ quần thể hiện tại; nếu không thỏa mãn điều kiện dừng quay lại bước 2 [...]... lần lặp, mỗi cá thể cập nhật tốc độ và vị trí của nó bằng cách theo dõi các vị trí tối ưu của cá thể đó và quần thể,  PSO có nhiều sự tương tự như kỹ thuật tính toán tiến hóa trong thuật toán di truyền GA (Genetic algorithm) Tuy nhiên, không giống như GA, PSO không có các thao tác tiến hóa như là lai ghép (crossover) hay đột biến (mutation) Công thức tính vận tốc, vị trí, trọng số quán tính: Vid =... 00110 6 -28 968 Thuật toán tối ưa hóa bầy đàn PSO (Particle swarm optimization)  PSO là một kỹ thuật tối ưu hóa ngẫu nhiên dựa trên một quần thể và sau đó tìm nghiệm tối ưu bằng cách cập nhật các thế hệ  Thuật toán PSO nghĩa là vị trí tối ưu của quần thể sẽ được cập nhật ngay lập tức khi một cá thể tìm thấy một vị trí tốt hơn so với vị trí tối ưu của quần thể trong việc tìm kiếm Thuật toán PSO đảm... quản lý bộ nhớ tự động Do đó có thể giải quyết nhiều vấn đề, bài toán với số dòng lệnh ít hơn so với các ngôn ngữ lập trình thường dùng như C,C++,…  Scilab được xây dựng dựa trên cơ sở của Matlab nên nó có cú pháp, câu lệnh tương tự với Matlab  Scilab được sử dụng rộng rãi trong các cơ sở giáo dục trung học và cao hơn cho giảng dạy toán học, khoa học kỹ thuật và kỹ thuật điều khiển tự động 2.Một số... lặp PID : Hàm mục tiêu Dùng tích phân của sai số tuyệt đối : F=1/J http://ieeexplore.ieee.org.scihub.org/xpl/articleDetails.jsp?tp=&arnumb er=6008424&queryText%3DPSO+Algorith m+in+pid+controller+on+fpga Scilab/Scicos Part 1: Scilab 1.Khái niệm và ứng dụng:  Là phần mềm mã nguồn mở miễn phí dùng cho việc tính toán số học và mô phỏng (tương thích Windows ,Linus ,Mac OS X ) ,cụ thể hơn là : xử lý tín . Giải thuật di truyền GA (Genetic Algorithms) GA bắt đầu với một tập hợp các giải pháp (được mã hóa bởi các nhiễm sắc thể (NST)) gọi. đến khi thỏa mãn một hoặc một số điều kiện dừng do chúng ta đặt ra. Các bước chính của giải thuật di truyền được mô tả vắn tắt như sau: 1. [Khởi đầu] Tạo ngẫu nhiên quần thể của N nhiễm sắc. 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