Phương pháp tối ưu hóa miễn dịch dựa trên xếp hạng đua thích nghi để giải quyết lập trình giá trị kỳ vọng đa mục tiêu

Soft Computing - Tập 22 - Trang 2139-2158 - 2017
Kai Yang1, Zhuhong Zhang2, Jiaxuan Lu2
1College of Computer Science & Technology, Guizhou University, Guiyang, China
2Department of Big Data Science and Engineering, College of Big Data and Information Engineering, Guizhou University, Guiyang, China

Tóm tắt

Nghiên cứu này điều tra một phương pháp tối ưu hóa miễn dịch lấy cảm hứng từ sinh học và phương pháp lấy mẫu thích nghi để giải quyết loại lập trình giá trị kỳ vọng phi tuyến đa mục tiêu mà không cần phân phối nhiễu trước. Đầu tiên, một ước lượng giới hạn dưới hữu ích được phát triển để hạn chế kích thước mẫu của các biến ngẫu nhiên. Thứ hai, một sơ đồ xếp hạng đua thích nghi được thiết kế để xác định những cá thể giá trị trong quần thể hiện tại, từ đó những cá thể chất lượng cao trong quá trình tìm kiếm giải pháp có thể nhận được kích thước mẫu lớn và mức độ quan trọng cao. Sau đó, một phương pháp tối ưu hóa lấy cảm hứng từ miễn dịch được xây dựng để tìm kiếm các giải pháp tối ưu $$\varepsilon $$-Pareto, dựa vào một mô hình bậc polymer hóa mới. Các thí nghiệm so sánh đã xác nhận rằng phương pháp đề xuất có hiệu quả cao, là một đơn vị tối ưu hóa cạnh tranh.

Từ khóa

#tối ưu hóa miễn dịch; lập trình giá trị kỳ vọng; phi tuyến; xếp hạng đua thích nghi; mẫu thích nghi; giải pháp tối ưu $$\varepsilon $$-Pareto

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