scholarly journals Multi-UAV Mission Allocation under Constraint

2019 ◽  
Vol 9 (11) ◽  
pp. 2184 ◽  
Author(s):  
Songyan Xie ◽  
An Zhang ◽  
Wenhao Bi ◽  
Yongchuan Tang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Time Window ◽  
Reduction Process ◽  
Target Area ◽  
Nsga Ii ◽  
Elite Strategy ◽  
Strategic Value ◽  
The Difference ◽  
Improved Model

This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method.

scholarly journals A complexity task of optimization in logistic distribution: A new approach to the green multi-objective vehicle routing problem

2022 ◽  
Vol 38 (1) ◽  
Author(s):  
Ferreira J. ◽  
Steiner M.
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Logistic Distribution ◽  
Nsga Ii ◽  
Routing Problem ◽  
Multi Objective ◽  
The Difference ◽  
Stage 1

Logistic distribution involves many costs for organizations. Therefore, opportunities for optimization in this respect are always welcome. The purpose of this work is to present a methodology to provide a solution to a complexity task of optimization in Multi-objective Optimization for Green Vehicle Routing Problem (MOOGVRP). The methodology, illustrated using a case study (employee transport problem) and instances from the literature, was divided into three stages: Stage 1, “data treatment”, where the asymmetry of the routes to be formed and other particular features were addressed; Stage 2, “metaheuristic approaches” (hybrid or non-hybrid), used comparatively, more specifically: NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOPSO (Multi-Objective Particle Swarm Optimization), which were compared with the new approaches proposed by the authors, CWNSGA-II (Clarke and Wright’s Savings with the Non-dominated Sorting Genetic Algorithm II) and CWTSNSGA-II (Clarke and Wright’s Savings, Tabu Search and Non-dominated Sorting Genetic Algorithm II); and, finally, Stage 3, “analysis of the results”, with a comparison of the algorithms. Using the same parameters as the current solution, an optimization of 5.2% was achieved for Objective Function 1 (OF{\displaystyle _{1}}; minimization of CO{\displaystyle _{2}} emissions) and 11.4% with regard to Objective Function 2 (OF{\displaystyle _{2}}; minimization of the difference in demand), with the proposed CWNSGA-II algorithm showing superiority over the others for the approached problem. Furthermore, a complementary scenario was tested, meeting the constraints required by the company concerning time limitation. For the instances from the literature, the CWNSGA-II and CWTSNSGA-II algorithms achieved superior results.

Research on Multi-Objective Transmission Planning in Electricity Markets

2012 ◽  
Vol 516-517 ◽  
pp. 1429-1432
Author(s):  
Yang Liu ◽  
Xu Liu ◽  
Feng Xian Cui ◽  
Liang Gao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Electricity Markets ◽  
Optimization Problem ◽  
Nsga Ii ◽  
Transmission Planning ◽  
Multi Objective ◽  
Complex Optimization ◽  
The Mathematical Model

Abstract. Transmission planning is a complex optimization problem with multiple deciding variables and restrictions. The mathematical model is non-linear, discrete, multi-objective and dynamic. It becomes complicated as the system grows. So the algorithm adopted affects the results of planning directly. In this paper, a fast non-dominated sorting genetic algorithm (NSGA-II) is employed. The results indicate that NSGA-II has some advantages compared to the traditional genetic algorithms. In transmission planning, NSGA-II is feasible, flexible and effective.

scholarly journals Supplier Selection and Production Planning by Using Guided Genetic Algorithm and Dynamic Nondominated Sorting Genetic Algorithm II Approaches

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
H. S. Wang ◽  
C. H. Tu ◽  
K. H. Chen
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Supplier Selection ◽  
Assembly Line Balancing ◽  
Assembly Sequence Planning ◽  
Maximum Efficiency ◽  
Nsga Ii ◽  
Defect Rate ◽  
The Mathematical Model ◽  
Nondominated Sorting

Through the global supply chain (SC), numerous firms participate in vertically integrated manufacturing, and industrial collaboration and cooperation is the norm. SC management activities, such as delivery time, quality, and defect rate, are characterized by uncertainty. Based on all of the aforementioned factors, this study established a multiobjective mathematical model, integrating the guided genetic algorithm (Guided-GA) and the nondominated sorting genetic algorithm II (NSGA-II), developed in previous studies, to improve the mechanisms of the algorithms, thereby increasing the efficiency of the model and quality of the solution. The mathematical model was used to address the problems of supplier selection, assembly sequence planning, assembly line balancing, and defect rate, to enable suppliers to respond rapidly to sales orders. The model was empirically tested using a case study, showing that it is suitable for assisting decision makers in planning production and conducting SS according to sales orders, enabling production activities to achieve maximum efficiency and the competitiveness of firms to improve.

scholarly journals SEARCH FOR THE OPTIMAL SOLUTION FOR ASSIGNING SUBURBAN-URBAN TRAINS ON BRANCHED LINES ON EACH OF THE POSSIBLE ROUTES

T-Comm ◽  
2020 ◽  
Vol 14 (11) ◽  
pp. 39-45
Author(s):  
Vadim N. Shmal ◽  
◽  
Liliya R. Aysina ◽  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimal Solution ◽  
Directed Search ◽  
Optimal Method ◽  
Source Data ◽  
On Line ◽  
The Difference ◽  
Traffic Organization ◽  
Selection Of

The article describes the problem of organizing train traffic on line with branches, analyzes possible separate routes on the "trunk" and "branch" sections, and brunched routes. The development of a mathematical model which consider the interests of passengers in a service provided and economic interest of passenger companies are elements of scientific novelty of presented researching. The insufficient disclosure in the existing theoretical base of the issues of train traffic organization on lines with branches confirms the relevance of the research. The deficit of clear requirements for the organization of such lines leads to empirical decisions when operating such facilities. Adhering to the goal of providing a comfortable service for passengers and taking into account economic aspects in the interests of suburban companies, the objective function is minimize the difference between the mileage of available seats (as a condition that reflects the interests of a commuter company) and the overcrowding of the train (as a criterion that ensures a comfortable ride for the passenger). The authors suggest using a genetic algorithm for solve this problem. Genetic algorithm is a tool that allows you to perform a directed search of competitive options. The article describes the method of applying the genetic algorithm, the formation of "parent" variants and "offspring" variants, compliance with a number of restrictions and ranking of offspring variants for the selection of the best "individuals". Before testing the proposed method on a real example, the authors make a preliminary analysis to identify possible shortcomings that require improvement. The possibility of using a mathematical model and a genetic algorithm to find the optimal (or close to optimal) option for organizing traffic on a line with branches is considered on an arbitrary example. The presented calculations can be performed in any analytical software package. In this research, the search for the optimal method to organize traffic on a line with branches was performed in the MS Excel product. Successful calculations on an arbitrary option using the proposed mathematical model and genetic algorithms (as a tool to determine the optimal option of movement), allows us to assume that calculations can be made based on real source data for suburban directions of railway junctions that have lines with branches.

The Research on the Optimization of Transportation Routing for Fresh Food by Improved Genetic Algorithm

2012 ◽  
Vol 178-181 ◽  
pp. 1790-1796 ◽  
Author(s):  
Ying Wu ◽  
Zi Bo Meng ◽  
Min Peng
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Customer Satisfaction ◽  
Time Window ◽  
Time Windows ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Transport Services ◽  
Fresh Food ◽  
Transportation Routing

In this paper, we research the problem of transportation routing for fresh food. We analyzed the limit of soft and hard time windows in transportation and formed the time window with fuzzy appointment based on customer satisfaction. The optimization of transportation routes mathematical model was structured. The improved genetic algorithm has been applied to matlab progam. This progam has found the optimal solution in the model. We used a case to prove the feasibility of the model and the algorithm. It has twelve customers and one DC need to transport services. The mathematical model is to simulate the transport of fresh food within realistic.The transportation routing is designed to improve customer satisfaction and reduce transportation costs.

Gravity Torque Balancing Device’s Optimization Design of Prosthesis’s Shoulder Based on Nonlinear Programming Genetic Algorithm

2012 ◽  
Vol 215-216 ◽  
pp. 479-483
Author(s):  
Bing Hui Fan ◽  
Cheng Zhi Yuan ◽  
Feng Min
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Nonlinear Programming ◽  
Optimization Design ◽  
Degrees Of Freedom ◽  
Matlab Program ◽  
The Difference ◽  
Process Diagrams ◽  
6 Degrees Of Freedom ◽  
Driving Torque

Robot prosthesis has 6 degrees of freedom, including outward swinging and forward and backward swinging, which are droved by servo motors. In order to reduce the servo motors’ driving torque, a gas spring was added on shoulder to balance the gravity torque. In this paper, the difference between gravity torque and balanced torque was created and taken as optimized object firstly, and then a mathematical model was created and Matlab program on the base of nonlinear programming genetic algorithm were made for obtaining the optimum, lastly the simulation model and optimizing process diagrams were outputted.

scholarly journals Research on the Station Layout Method of Ground-Based Pseudolite Positioning System Based on NSGA-II Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Li Yang ◽  
Kaiyuan Yang ◽  
Danshi Sun
Keyword(s):  
Genetic Algorithm ◽  
Stationary Distribution ◽  
Base Station ◽  
Positioning System ◽  
Nsga Ii ◽  
Positioning Accuracy ◽  
Elite Strategy ◽  
System Method ◽  
Signal Coverage ◽  
Visual Domain

Given the problem that the existing method of station distributing the pseudosatellite system cannot ensure both its coverage and position in a situation of signal occlusion, it proposed a new stationary layout method with an elite strategy for a ground-based pseudosatellite positioning system based on the elite strategy of the nondominant genetic rankings (NSGA-II). The geometrical design of the pseudosatellite system is calculated by visual domain analysis and precision factors for the signal coverage age and base station. To optimize the algorithm, the NSGA-II algorithm is used. An earth pseudosatellite positioning system method of stationary distribution is obtained that simultaneously optimizes signal coverage and positioning accuracy. The algorithm is better distributed and has a certain superintendence compared with the traditional genetic algorithm.

A New Transmission Network Planning Method with Consideration of Transmission Cost

2013 ◽  
Vol 316-317 ◽  
pp. 132-135
Author(s):  
Xu Liu ◽  
Cui Lian Tang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Problem ◽  
Transmission Network ◽  
Nsga Ii ◽  
Transmission Planning ◽  
Transmission Cost ◽  
Transmission Network Planning ◽  
Complex Optimization ◽  
The Mathematical Model

Transmission planning is a complex optimization problem with multiple deciding variables and restrictions. The mathematical model is non-linear, discrete, multi-objective and dynamic. It becomes complicated as the system grows. So the algorithm adopted affects the results of planning directly. In this paper, a fast non-dominated sorting genetic algorithm (NSGA-II) is employed. The results indicate that NSGA-II has some advantages compared to the traditional genetic algorithms. In transmission planning, NSGA-II is feasible, flexible and effective.

Designing a Bi-Objective Closed-Loop Supply Chain Problem with Shortage and All Unit Discount: “Nondominated Sorting Genetic Algorithm II” and “Multi-Objective Particle Swarm Optimization”

2020 ◽  
Vol 19 (04) ◽  
pp. 701-736
Author(s):  
Masoomeh Vazifeh Pirnagh ◽  
Hamed Davari-Ardakani ◽  
Seyed Hamid Reza Pasandideh
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Closed Loop ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Closed Loop Supply Chain ◽  
The Real ◽  
Multi Objective

Nowadays, due to environmental issues, government rules and economic interests have increased attention to the collection and recovery of products, which has led to the formation of new concepts such as reverse and closed-loop supply chains. The implementation of the closed-loop supply chain as a solution to sustainable development is expanding from one hand and increasing the profitability of companies on the other. For this purpose, a mathematical model was developed to design an integrated closed-loop supply chain network, which is a combination of two-problem localization problems and flow optimization. The proposed model was designed to minimize network costs and to maximize the level of responsiveness to customers. The cost parameters of establishing centers in this model are uncertain; to overcome the model’s uncertainties, stochastic programming is used. In the mathematical model, supplier, manufacturer, distributor and customer in the direct supply chain and collection/rehabilitation, destruction, recycling centers and, second-type distribution center for sale of second-hand products as well as second-hand products customers in the reverse flow are considered, to be closer to the real today world. This model is multi-periodic mix integer nonlinear programming where the shortage has allowed. To motivate and encourage customers to buy more, in addition to getting closer to the real world and it happens more in practice, is considered all units of discount for transportation cost in the forward flow. To solve this model Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) is using. The parameter tuning was done using the Taguchi method. Then, the important criteria for measurement and comparison of performance algorithms have used, including the Mean Ideal Distance, Diversification Metric, Number of Pareto-optimal Solutions, and the Quality Metric. Results of the Comparative metrics show that NSGA-II outperforms MOPSO in almost all cases in achieving the best trade-off solutions.

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