Non dominated sorting genetic algorithm

Mục tiêu của đề tài là thiết lập bài toán tối ưu hóa đa mục tiêu cho kết cấu dầm composite với hai hàm mục tiêu là cực tiểu khối lượng và cực tiểu chuyển vị; tìm hiểu giải thuật NSGA - II (Elitist Non - Dominated Sorting Genetic Algorithm); nghiên cứu bổ sung ràng buộc về tỷ lệ phân bố lượng sợi trong từng lớp rf trong quá trình sản xuất; áp dụng thuật toán NSGA-II để tìm lời giải tối ưu của ba bài toán được thiết lập.

101p beloveinhouse10 21-09-2021 27 5   Download

This paper presents a model to solve the multi-objective location-routing problem with capacitated vehicles. The main purposes of the model are to find the optimal number and location of depots, the optimal number of vehicles, and the best allocation of customers to distribution centers and to the vehicles.

16p tocectocec 24-05-2020 7 2   Download

This research proposes a model to optimize a freight-scheduling problem. The proposed model of this paper based on Non-dominated sorting genetic algorithm-II is formulated to solve a conflicting bi-objective optimization and optimizes a real-world case study.

16p tohitohi 22-05-2020 24 3   Download

This paper introduces a building information modeling (BIM)-based model to evaluate the environmental and economic consequences of different project alternatives. The model calculates direct, indirect emissions and primary energy for the overall project life cycle.

20p tohitohi 22-05-2020 11 0   Download

A non-dominated sorting genetic algorithm (NSGA-II) is applied to obtain Pareto optimal solutions in widely used advanced machining processes, i.e., electric discharge machining, electrochemical micromachining, ultrasonic machining, abrasive water jet machining.

10p tohitohi 22-05-2020 47 2   Download

(BQ) This paper reports an intelligent approach for process modeling and optimization of electric discharge machining (EDM). Physics based process modeling using finite element method (FEM) has been integrated with the soft computing techniques like artificial neural networks (ANN) and genetic algorithm (GA) to improve prediction accuracy of the model with less dependency on the experimental data.

13p xuanphuongdhts 27-03-2017 56 2   Download