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Genetic algorithm
An application of genetic Algorithm into order scheduling of a textile companyThis research paper addresses scheduling issues that arising in the dyeing plant, for the product of shoelaces with various restrictions such as color, machines resource and the demand uncertainty.
Bài giảng Máy học nâng cao: Genetic algorithm - Trịnh Tấn ĐạtBài giảng "Máy học nâng cao: Genetic algorithm" cung cấp cho người học các kiến thức: Introduction - Genetic algorithm (GA), genetic algorithm operators and parameters, example. Cuối bài giảng có phần bài tập để người học ôn tập và củng cố kiến thức.
70 trang
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Bài giảng Khai phá dữ liệu (Data mining): Genetic algorithm - Trịnh Tấn ĐạtBài giảng Khai phá dữ liệu (Data mining): Genetic algorithm, chương này trình bày những nội dung về: introduction to genetic algorithms (GA); classes of search techniques; nature to computer mapping; GA operators and parameters; example and homework;... Mời các bạn cùng tham khảo chi tiết nội dung bài giảng!
70 trang
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Bi-objective freight scheduling optimization in an integrated forward/reverse logistic network using non-dominated sorting genetic algorithm-IIThis 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.
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The solution to the project scheduling problem by using an improved genetic algorithmTo solve this problem, we use an improved genetic algorithm named GA-RT (Genetic Algorithm with Random Crossover and Negative Tournament Selection) and conduct experiments on the iMOPSE standard dataset. Experimental results show that the proposed GA-RT algorithm can effectively solve the project scheduling problem, achieving better performance compared to existing algorithms.
10 trang
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Optimal coordination of directional overcurrent protection relays using genetic algorithmThis paper studies on establishing the mathematical optimization formulation to coordinate the operating times of directional overcurrent relays in transmission networks. The objective function of this optimization formulation is to minimize the total operation time of all directional overcurrent relays, and it must satisfy the highly nonlinear constraints such as the time multiplier setting range, the plug setting range, and the coordination time interval between the primary and backup relay pair.
9 trang
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Optimization of sliding control parameters for a 3-DOF robot ARM using genetic algorithm (GA)This paper presents a method for optimizing the sliding mode control (SMC) parameter for a robot manipulator applying a genetic algorithm (GA). The objective of the SMC is to achieve precise and consistent tracking of the trajectory of the robot manipulator under uncertain and disturbed conditions.
6 trang
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A novel particle swarm optimization guided genetic to the distribution network reconfiguration problem with an objective function of minimum operating and power outage costsIn operating the distribution network, the problem of reconfiguration distribution network according to the change of load to reduce power loss has partly reduced the operation cost of the distribution network but it can impact the reliability of power supply to the load.
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Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm(BQ) The present work is aimed at optimizing the surface roughness of die sinking electric discharge machining (EDM) by considering the simultaneous affect of various input parameters.
9 trang
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Optimizing axial bearing capacity estimation of prebored grouted planted nodular (PGPN) pile: Enriched dataset and genetic algorithm approachThis study introduces a practical and efficient methodology to improve the estimation of the ultimate axial bearing capacity of Pre-bored Grouted Planted Nodular (PGPN) piles, addressing a critical need in geotechnical engineering.
25 trang
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0 The Optimization of the Electro-Discharge Machining Process Using Response(BQ) In this paper, working current, working voltage, oil pressure, spark gap Pulse On Time and Pulse Off Time on Material Removal Rate (MRR) and Surface Finish (Ra) has been studied. Empirical models for MRR and Ra have been developed by conducting a designed experiment based on the Grey Relational Analysis. Genetic Algorithm (GA) based multi-objective optimization for maximization of MRR and minimization of Ra has been done by using the developed empirical models. Optimization
results have been used for identifying the machining conditions. For verification of the empirical models and the optimization results, focused experiments have been conducted in the rough and finish machining regions.
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An innovative genetic algorithm-based master schedule to optimize job shop scheduling problemThe rapid development of manufacturing sectors in the contemporary Industry 4.0 era has prompted enterprises to adopt increasingly systematic, streamlined, and efficient organizational structures. In this scenario, production scheduling has arisen as an essential function that contributes to the effective allocation of organizational resources in the production process, while shortening production time and satisfying customer deadlines.
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Optimizing the architecture of the artificial neural network by genetic algorithm to improve the predictability of pile bearing capacity based on CPT resultsThis paper presents the results of applying the Artificial Neural Network (ANN) model in determining pile bearing capacity. The traditional methods used to calculate the bearing capacity of piles still have many disadvantages that need to be overcome such as high cost, complicated calculation, time-consuming.
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Multi-objective optimization of selected non-traditional machining processes using NSGA-IIA 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.
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Genetic algorithm rule based categorization method for textual data miningThis paper proposed a hybrid GA rule based categorization method, named genetic algorithm rule based categorization (GARC), to enhance the accuracy of categorization rules and to produce accurate classifier for text mining.
14 trang
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A hybrid fuzzy-optimization method for modeling construction emissionsThis 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.
20 trang
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Bài giảng Máy học nâng cao: Genetic algorithm - Trịnh Tấn Đạt (2024)Bài giảng "Máy học nâng cao: Genetic algorithm" cung cấp cho người đọc các nội dung: Introduction - Genetic algorithm (GA), Genetic algorithm operators and parameters, example, homework. Mời các bạn cùng tham khảo nội dung chi tiết.
70 trang
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A multi-depot location routing problem to reduce the differences between the vehicles’ traveled distances; a comparative study of heuristicsThis 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.
16 trang
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Intelligent process modeling and optimization of die-sinking electric discharge machining(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.
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Bài giảng Tính toán tiến hóa: Bài 2 - TS. Huỳnh Thị Thanh BìnhBài giảng "Tính toán tiến hóa: Bài 2 - TS. Genetic Algorithm (GA)" được biên soạn với các nội dung chính sau đây: Tổng quan về thuật toán Genetic Algorithm; Sơ đồ thuật toán GAs; Các thành phần của GA;... Mời các bạn cùng tham khảo bài giảng tại đây!
45 trang
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