scholarly journals Use of a evolutionary algorithm to optimize interplanetary transfers

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Guilherme Marcos Neves ◽  
Denilson Paulo Souza dos Santos
Keyword(s):  
Genetic Algorithm ◽  
Numerical Methods ◽  
Space Vehicle ◽  
Close Approach ◽  
Low Thrust ◽  
Gravity Assist ◽  
Total Impulse ◽  
Gravity Assist Maneuver ◽  
The Cost ◽  
Interplanetary Missions

In this paper, it was studied the optimization of the cost of interplanetary missions with emphasis on reducing fuel consumption. To achieve this goal, a genetic algorithm was implemented to optimize the total impulse of orbital transfer. It was implemented a case of sending a space vehicle from Earth to a another planet using a gravity assist maneuver (swing by), in this paper it was chose sending a spacecraft from Earth to Mars with a close approach to the Venus. The method employed can be used for impulsive interplanetary missions in general, and so the solution found can become an initial solution for numerical methods of optimization of low thrust maneuvers

Efficient Genetic Algorithms Using Discretization Scheduling

2005 ◽  
Vol 13 (3) ◽  
pp. 353-385 ◽  
Author(s):  
Laura A. McLay ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Numerical Methods ◽  
Computation Time ◽  
Convergence Time ◽  
Function Evaluation ◽  
Two Dimensional ◽  
Time Analysis ◽  
The Cost ◽  
Speed And Accuracy

In many applications of genetic algorithms, there is a tradeoff between speed and accuracy in fitness evaluations when evaluations use numerical methods with varying discretization. In these types of applications, the cost and accuracy vary from discretization errors when implicit or explicit quadrature is used to estimate the function evaluations. This paper examines discretization scheduling, or how to vary the discretization within the genetic algorithm in order to use the least amount of computation time for a solution of a desired quality. The effectiveness of discretization scheduling can be determined by comparing its computation time to the computation time of a GA using a constant discretization. There are three ingredients for the discretization scheduling: population sizing, estimated time for each function evaluation and predicted convergence time analysis. Idealized one- and two-dimensional experiments and an inverse groundwater application illustrate the computational savings to be achieved from using discretization scheduling.

Rendez vous with Saturn's rings

1984 ◽  
Vol 75 ◽  
pp. 743-759 ◽  
Author(s):  
Kerry T. Nock
Keyword(s):  
Solar Energy ◽  
Electric Propulsion ◽  
Power Source ◽  
Propulsion System ◽  
Low Thrust ◽  
Ring Plane ◽  
The Earth ◽  
Total Impulse ◽  
Saturn's Rings

ABSTRACTA mission to rendezvous with the rings of Saturn is studied with regard to science rationale and instrumentation and engineering feasibility and design. Future detailedin situexploration of the rings of Saturn will require spacecraft systems with enormous propulsive capability. NASA is currently studying the critical technologies for just such a system, called Nuclear Electric Propulsion (NEP). Electric propulsion is the only technology which can effectively provide the required total impulse for this demanding mission. Furthermore, the power source must be nuclear because the solar energy reaching Saturn is only 1% of that at the Earth. An important aspect of this mission is the ability of the low thrust propulsion system to continuously boost the spacecraft above the ring plane as it spirals in toward Saturn, thus enabling scientific measurements of ring particles from only a few kilometers.

scholarly journals Inclusion of Hydraulic Controls in Rehabilitation Models of Drainage Networks to Control Floods

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 514
Author(s):  
Leonardo Bayas-Jiménez ◽  
F. Javier Martínez-Solano ◽  
Pedro L. Iglesias-Rey ◽  
Daniel Mora-Melia ◽  
Vicente S. Fuertes-Miquel
Keyword(s):  
Genetic Algorithm ◽  
Drainage System ◽  
Optimal Solution ◽  
Hydraulic Control ◽  
Drainage Systems ◽  
Storm Water Management Model ◽  
Drainage Networks ◽  
Extreme Rain ◽  
Rain Events ◽  
The Cost

A problem for drainage systems managers is the increase in extreme rain events that are increasing in various parts of the world. Their occurrence produces hydraulic overload in the drainage system and consequently floods. Adapting the existing infrastructure to be able to receive extreme rains without generating consequences for cities’ inhabitants has become a necessity. This research shows a new way to improve drainage systems with minimal investment costs, using for this purpose a novel methodology that considers the inclusion of hydraulic control elements in the network, the installation of storm tanks and the replacement of pipes. The presented methodology uses the Storm Water Management Model for the hydraulic analysis of the network and a modified Genetic Algorithm to optimize the network. In this algorithm, called the Pseudo-Genetic Algorithm, the coding of the chromosomes is integral and has been used in previous studies of hydraulic optimization. This work evaluates the cost of the required infrastructure and the damage caused by floods to find the optimal solution. The main conclusion of this study is that the inclusion of hydraulic controls can reduce the cost of network rehabilitation and decrease flood levels.

scholarly journals Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

2019 ◽  
Vol 11 (9) ◽  
pp. 2571
Author(s):  
Xujing Zhang ◽  
Lichuan Wang ◽  
Yan Chen
Keyword(s):  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Optimal Solution ◽  
Material Distribution ◽  
Low Carbon ◽  
Nsga Ii ◽  
Management Objectives ◽  
Multi Objective Genetic Algorithm ◽  
Distribution Process ◽  
The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

scholarly journals A Heuristics-Based Parthenogenetic Algorithm for the VRP with Potential Demands and Time Windows

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Chenghua Shi ◽  
Tonglei Li ◽  
Yu Bai ◽  
Fei Zhao
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Computation Time ◽  
Routing Problem ◽  
Split Delivery ◽  
Soft Time Windows ◽  
Comparison Results ◽  
Potential Demand ◽  
The Cost

We present the vehicle routing problem with potential demands and time windows (VRP-PDTW), which is a variation of the classical VRP. A homogenous fleet of vehicles originated in a central depot serves customers with soft time windows and deliveries from/to their locations, and split delivery is considered. Also, besides the initial demand in the order contract, the potential demand caused by conformity consuming behavior is also integrated and modeled in our problem. The objective of minimizing the cost traveled by the vehicles and penalized cost due to violating time windows is then constructed. We propose a heuristics-based parthenogenetic algorithm (HPGA) for successfully solving optimal solutions to the problem, in which heuristics is introduced to generate the initial solution. Computational experiments are reported for instances and the proposed algorithm is compared with genetic algorithm (GA) and heuristics-based genetic algorithm (HGA) from the literature. The comparison results show that our algorithm is quite competitive by considering the quality of solutions and computation time.

scholarly journals A multivariable multiobjective predictive controller

2013 ◽  
Vol 23 (1) ◽  
pp. 35-45 ◽  
Author(s):  
Faten Ben Aicha ◽  
Faouzi Bouani ◽  
Mekki Ksouri
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Mimo System ◽  
Weighting Factor ◽  
Nsga Ii ◽  
Weighted Sum Method ◽  
Prediction Horizon ◽  
Predictive Controller ◽  
Pareto Method ◽  
The Cost

Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the Non- Dominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.

scholarly journals Optimized Placement of Connecting the Distributed Generationswork Stand Alone to Improve the Distribution Systems Reliability

2013 ◽  
Vol 64 (2) ◽  
pp. 76-83
Author(s):  
Hamed Hashemi-Dezaki ◽  
Ali Agheli ◽  
Behrooz Vahidi ◽  
Hossein Askarian-Abyaneh
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
System Reliability ◽  
Distribution Systems ◽  
Analytical Approach ◽  
Distributed Generations ◽  
Joint Point ◽  
Cost Of Energy ◽  
Optimum Solution ◽  
The Cost

The use of distributed generations (DGs) in distribution systems has been common in recent years. Some DGs work stand alone and it is possible to improve the system reliability by connecting these DGs to system. The joint point of DGs is an important parameter in the system designing. In this paper, a novel methodology is proposed to find the optimum solution in order to make a proper decision about DGs connection. In the proposed method, a novel objective function is introduced which includes the cost of connector lines between DGs and network and the cost of energy not supplied (CENS) savings. Furthermore, an analytical approach is used to calculate the CENS decrement. To solve the introduced nonlinear optimization programming, the genetic algorithm (GA) is used. The proposed method is applied to a realistic 183-bus system of Tehran Regional Electrical Company (TREC). The results illustrate the effectiveness of the method to improve the system reliability by connecting the DGs work stand alone in proper placements.

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