scholarly journals Using Genetic Algorithms to Solve Large-scale Airline Network Planning Problems

2015 ◽  
Vol 10 ◽  
pp. 900-909 ◽  
Author(s):  
Katrin Kölker ◽  
Klaus Lütjens
Keyword(s):  
Genetic Algorithms ◽  
Large Scale ◽  
Network Planning ◽  
Airline Network ◽  
Planning Problems

Methods of Network Planning and Management when Implementing Territory Planning Projects

2019 ◽  
pp. 49-54
Keyword(s):  
Large Scale ◽  
Housing Stock ◽  
Network Planning ◽  
Network Models ◽  
Point Of View ◽  
Residential Areas ◽  
Basic Principles ◽  
Planning And Management ◽  
Planning Project ◽  
Complex Process

The success of the Program of housing stock renovation in Moscow depends on the efficiency of resource management. One of the main urban planning documents that determine the nature of the reorganization of residential areas included in the Program of renovation is the territory planning project. The implementation of the planning project is a complex process that has a time point of its beginning and end, and also includes a set of interdependent parallel-sequential activities. From an organizational point of view, it is convenient to use network planning and management methods for project implementation. These methods are based on the construction of network models, including its varieties – a Gantt chart. A special application has been developed to simulate the implementation of planning projects. The article describes the basic principles and elements of modeling. The list of the main implementation parameters of the Program of renovation obtained with the help of the developed software for modeling is presented. The variants of using the results obtained for a comprehensive analysis of the implementation of large-scale urban projects are proposed.

scholarly journals Optimizing operation of a large-scale pumped storage hydropower system coordinated with wind farm by means of genetic algorithms

2019 ◽  
Keyword(s):  
Genetic Algorithms ◽  
Renewable Energy ◽  
Wind Energy ◽  
Large Scale ◽  
Wind Farm ◽  
Closed Loop ◽  
Open Loop ◽  
Energy Output ◽  
Reservoir System ◽  
Pumped Storage

<p>Due to the intermittent and fluctuating nature of wind and other renewable energy sources, their integration into electricity systems requires large-scale and flexible storage systems to ensure uninterrupted power supply and to reduce the percentage of produced energy that is discarded or curtailed. Storage of large quantities of electricity in the form of dynamic energy of water masses by means of coupled reservoirs has been globally recognized as a mature, competitive and reliable technology; it is particularly useful in countries with mountainous terrain, such as Greece. Its application may increase the total energy output (and profit) of coupled wind-hydroelectric systems, without affecting the availability of water resources. Optimization of such renewable energy systems is a very complex, multi-dimensional, non-linear, multi modal, nonconvex and dynamic problem, as the reservoirs, besides hydroelectric power generation, serve many other objectives such as water supply, irrigation and flood mitigation. Moreover, their function should observe constraints such as environmental flow. In this paper we developed a combined simulation and optimization model to maximize the total benefits by integrating wind energy production into a pumped-storage multi-reservoir system, operating either in closed-loop or in open-loop mode. In this process, we have used genetic algorithms as the optimization tool. Our results show that when the operation of the reservoir system is coordinated with the wind farm, the hydroelectricity generation decreases drastically, but the total economical revenue of the system increases by 7.02% when operating in closed-loop and by 7.16% when operating in open-loop mode. We conclude that the hydro-wind coordination can achieve high wind energy penetration to the electricity grid, resulting in increase of the total benefits of the system. Moreover, the open-loop pumped-storage multi-reservoir system seems to have better performance, ability and flexibility to absorb the wind energy decreasing to a lesser extent the hydroelectricity generation, than the closed-loop.</p>

Airline Network Planning: Mixed-integer non-convex optimization with demand–supply interactions

2021 ◽  
Vol 154 ◽  
pp. 100-124
Author(s):  
Sebastian Birolini ◽  
Alexandre Jacquillat ◽  
Mattia Cattaneo ◽  
António Pais Antunes
Keyword(s):  
Convex Optimization ◽  
Network Planning ◽  
Mixed Integer ◽  
Airline Network

Recursive Learning of Genetic Algorithms with Task Decomposition and Varied Rule Set

2013 ◽  
pp. 226-247
Author(s):  
Lei Fang ◽  
Sheng-Uei Guan ◽  
Haofan Zhang
Keyword(s):  
Genetic Algorithms ◽  
Classification Accuracy ◽  
Large Scale ◽  
Chromosome Length ◽  
Task Decomposition ◽  
Rule Set ◽  
Rule Number ◽  
Algorithmic Framework ◽  
Recursive Learning ◽  
Generalization Accuracy

Rule-based Genetic Algorithms (GAs) have been used in the application of pattern classification (Corcoran & Sen, 1994), but conventional GAs have weaknesses. First, the time spent on learning is long. Moreover, the classification accuracy achieved by a GA is not satisfactory. These drawbacks are due to existing undesirable features embedded in conventional GAs. The number of rules within the chromosome of a GA classifier is usually set and fixed before training and is not problem-dependent. Secondly, conventional approaches train the data in batch without considering whether decomposition solves the problem. Thirdly, when facing large-scale real-world problems, GAs cannot utilise resources efficiently, leading to premature convergence. Based on these observations, this paper develops a novel algorithmic framework that features automatic domain and task decomposition and problem-dependent chromosome length (rule number) selection to resolve these undesirable features. The proposed Recursive Learning of Genetic Algorithm with Task Decomposition and Varied Rule Set (RLGA) method is recursive and trains and evolves a team of learners using the concept of local fitness to decompose the original problem into sub-problems. RLGA performs better than GAs and other related solutions regarding training duration and generalization accuracy according to the experimental results.

Path Planning in a Mobile Robot

2019 ◽  
pp. 582-608
Author(s):  
Diego Alexander Tibaduiza Burgos ◽  
Maribel Anaya Vejar
Keyword(s):  
Genetic Algorithms ◽  
Path Planning ◽  
Mobile Robot ◽  
Fitness Function ◽  
Experimental Setup ◽  
Robot Path Planning ◽  
Natural Evolution ◽  
On Line ◽  
Planning Problems ◽  
Robot Path

This chapter presents the development and implementation of three approaches that contribute to solving the mobile robot path planning problems in dynamic and static environments. The algorithms include some items regarding the implementation of on-line and off-line situations in an environment with static and mobile obstacles. A first technique involves the use of genetic algorithms where a fitness function and the emulation of the natural evolution are used to find a free-collision path. The second and third techniques consider the use of potential fields for path planning using two different ways. Brief descriptions of the techniques and experimental setup used to test the algorithms are also included. Finally, the results applying the algorithms using different obstacle configurations are presented and discussed.

Comparative Study on Multi-Objective Genetic Algorithms for Seismic Response Controls of Structures

2013 ◽  
pp. 333-358
Author(s):  
Young-Jin Cha ◽  
Yeesock Kim
Keyword(s):  
Genetic Algorithms ◽  
Control System ◽  
Large Scale ◽  
The Other ◽  
Seismic Excitations ◽  
Civil Structures ◽  
Multi Objective ◽  
Strength Pareto Evolutionary Algorithm ◽  
Structural Responses ◽  
New Algorithms

This chapter introduces three new multi-objective genetic algorithms (MOGAs) for minimum distributions of both actuators and sensors within seismically excited large-scale civil structures such that the structural responses are also minimized. The first MOGA is developed through the integration of Implicit Redundant Representation (IRR), Genetic Algorithm (GA), and Non-dominated sorting GA 2 (NSGA2): NS2-IRR GA. The second one is proposed by combining the best features of both IRR GA and Strength Pareto Evolutionary Algorithm (SPEA2): SP2-IRR GA. Lastly, Gene Manipulation GA (GMGA) is developed based on novel recombination and mutation mechanism. To demonstrate the effectiveness of the proposed three algorithms, two full-scale twenty-story buildings under seismic excitations are investigated. The performances of the three new algorithms are compared with the ones of the ASCE benchmark control system while the uncontrolled structural responses are used as a baseline. It is shown that the performances of the proposed algorithms are slightly better than those of the benchmark control system. In addition, GMGA outperforms the other genetic algorithms.

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