Expert system using multi-objective optimization of the direct current railway power supply system

There are many different aspects to be analyzed when designing a railway infrastructure. The energy system, which withstands the demand for energy from operating trains, must consider many factors to create a functional infrastructure, in terms of demanded energy and cost sustainable. The methodology proposed gives a set of possible solutions to the designer or engineer. On the one hand, this method works with a multi-objective genetic algorithm (NSGA-II), with high time efficiency. The main target of this work is to obtain the best electrical configuration in terms of number and location of substations and characteristics of the overhead line system. On the other hand, best configurations must take into account things such as real railway operation, signalling system, infrastructure, costs linked with environment, maintenance, construction and connection with general electric network, losses of energy dissipated along the catenary. Hence, this methodology must combine all of these skills and integrate it with a railway configuration, modelling and simulation tool, Hamlet developed at CITEF (Research Centre on Railway Technologies by Technical University of Madrid, Spain). After using this methodology, designers will have a set of configurations in order to get the final choice of location of traction substations and type of overhead line system to achieve properly the power demand from trains in railway systems.

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Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Energies ◽

10.3390/en14154466 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4466

Author(s):

Maël Riou ◽

Florian Dupriez-Robin ◽

Dominique Grondin ◽

Christophe Le Loup ◽

Michel Benne ◽

...

Keyword(s):

Power Systems ◽

Rural Areas ◽

Energy System ◽

Design Stage ◽

Multi Objective Optimization ◽

Renewable Energy Resources ◽

Nsga Ii ◽

Renewable Integration ◽

Multi Objective ◽

Trade Offs

Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.

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Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

Nature-Inspired Computing Design, Development, and Applications ◽

10.4018/978-1-4666-1574-8.ch010 ◽

2012 ◽

pp. 179-200 ◽

Cited By ~ 1

Author(s):

Cristiane G. Taroco ◽

Eduardo G. Carrano ◽

Oriane M. Neto

Keyword(s):

Power Distribution ◽

Distribution Systems ◽

Search Algorithm ◽

Efficient Solutions ◽

Optimization Techniques ◽

Nsga Ii ◽

Robust Solutions ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Reliability And Robustness

The growing importance of electric distribution systems justifies new investments in their expansion and evolution. It is well known in the literature that optimization techniques can provide better allocation of the financial resources available for such a task, reducing total installation costs and power losses. In this work, the NSGA-II algorithm is used for obtaining a set of efficient solutions with regard to three objective functions, that is cost, reliability, and robustness. Initially, a most likely load scenario is considered for simulation. Next, the performances of the solutions achieved by the NSGA-II are evaluated under different load scenarios, which are generated by means of Monte Carlo Simulations. A Multi-objective Sensitivity Analysis is performed for selecting the most robust solutions. Finally, those solutions are submitted to a local search algorithm to estimate a Pareto set composed of just robust solutions only.

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MULTI-OBJECTIVE OPTIMISATION AND ITS APPLICATION TO CONCEPT DEVELOPMENT IN FLOATING OFFSHORE SYSTEMS

The International Journal of Maritime Engineering ◽

10.5750/ijme.v162ia4.1147 ◽

2021 ◽

Vol 162 (A4) ◽

Author(s):

Dr P Gallagher

Keyword(s):

Economic Performance ◽

Concept Development ◽

Oil Field ◽

Rational Approach ◽

Nsga Ii ◽

Investment Performance ◽

Field Development ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Floating System

This paper addresses the need for a rapid, multi-disciplined and rational approach to floating system concept development and selection during the very earliest stages of project definition. It describes the implementation of a modified multi- objective Genetic Algorithm for this purpose. A formulation of the NSGA-II algorithm is combined with additional Target Functions to reduce otherwise large multi-disciplined problems to more tractable solution using tools commonly available in the design office. It also provides a rational basis for the comparison of different design solutions each of which are Pareto Optimal with respect to the technical and economic performance of each underlying concept. A specific example of marginal field development using a novel FPSO concept is presented. Starting with just the oil field location and reserves estimate, the algorithm provides the means to define preliminary hull form and production facility capacities, match performance to payload, and give preliminary indicators of likely investment performance. The method may also be applied more generally in preliminary ship design, particularly where it is possible to model economic performance alongside efficiency, safety and key technical factors in hydrodynamics and structures.

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EFFICIENCY INCREASE OF MULTI-OBJECTIVE GENETIC ALGORITHMS USING THE CLUSTERIZATION OF A POPULATION IN THE VARIABLE SPACE

Vestnik komp iuternykh i informatsionnykh tekhnologii ◽

10.14489/vkit.2019.10.pp.013-020 ◽

2019 ◽

pp. 13-20

Author(s):

P. V. Kazakov

Keyword(s):

Genetic Algorithm ◽

Benchmark Problems ◽

Additional Parameter ◽

Nsga Ii ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Fitness Value ◽

Efficiency Increase ◽

Variable Space ◽

Special Rule

The paper introduces a new manner for improving of obtained by MOGA (Multi-Objective Genetic Algorithm) solutions. It is based on the concept of dividing the population into set of clusters according to solutions similarity. In different of most MOGA the clusterization of population is implemented in the variable space, enables to enhance diversity of population and to increase the number of non-dominated solutions. The special procedures for the clustering of current population and copying the clusters in the next population were developed. The dominance principal by fitness-value is used for clustering. The number of clusters depends on additional parameter the radius of cluster’s hypersphere that is determined experimentally. By the special rule the individuals corresponded to centroids of clusters are copied in the new population. The clusters are recalculated for every population. The influence of the radius cluster to the number of non-dominated solutions variation was studied. The cluster modification should be integrated into any multi-objective genetic algorithm. By the analytical evaluation has been studied, this MOGA modification has additional computationally complexity from linear to quadratic. In experiments it was tested with the evolutionary algorithms SPEA2, NSGA-II on the special benchmark problems (DTLZ) with a various number of criteria using the set of performance indices. The used clustering in the variable space algorithms were achieved a better distribution and convergence to the true Paretofront in some cases.

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Applications of Multi-Objective Genetic Algorithm in Optimizing Steering Performances for Submersible

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3136 ◽

2013 ◽

Vol 756-759 ◽

pp. 3136-3140

Author(s):

Zhuo Yi Yang ◽

Yong Jie Pang ◽

Shao Lian Ma

Keyword(s):

Genetic Algorithm ◽

Optimal Design ◽

Approximation Model ◽

Horizontal Movement ◽

Nsga Ii ◽

Hydrodynamic Coefficient ◽

Pareto Solution ◽

Multi Objective ◽

Minimum Drag ◽

Multi Objective Genetic Algorithm

Multi-objective arithmetic NSGA-II based on Pareto solution is investigated to deal with integrated optimal design of speedability and manoeuvre performances for submersible. Approximation model of resistance for serial revolving shape is constructed by hydrodynamic numerical calculations. The appraisement criterions of stability and mobility are calculated from linear equation of horizontal movement by estimating hydrodynamic coefficient of submersible. After optimization, the scattered Pareto solution of drag and turning diameter are gained, and from the solutions designer can select the reasonable one based on the actual requirement. The Pareto solution can ensure the minimum drag in this manoeuvre performance or the best manoeuvre performance in this drag value.

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Co-optimization of carbon dioxide storage and enhanced oil recovery in oil reservoirs using a multi-objective genetic algorithm (NSGA-II)

Petroleum Science ◽

10.1007/s12182-014-0362-1 ◽

2014 ◽

Vol 11 (3) ◽

pp. 460-468 ◽

Cited By ~ 11

Author(s):

Mohammad Amin Safarzadeh ◽

Seyyed Mahdia Motahhari

Keyword(s):

Carbon Dioxide ◽

Genetic Algorithm ◽

Enhanced Oil Recovery ◽

Oil Recovery ◽

Oil Reservoirs ◽

Carbon Dioxide Storage ◽

Nsga Ii ◽

Multi Objective ◽

Multi Objective Genetic Algorithm

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Optimization of Force and Moment Balance of a Four-Bar Linkage Using Multi-Objective Genetic Algorithm

Volume 4: Design and Manufacturing ◽

10.1115/imece2009-12740 ◽

2009 ◽

Author(s):

Mohammad Reza Farmani ◽

A. Jaamiolahmadi

Keyword(s):

Genetic Algorithm ◽

Objective Functions ◽

Fuzzy Decision ◽

Nsga Ii ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Feasible Solutions ◽

Four Bar Linkage ◽

External Loads ◽

Vibration Noise

In this study, force and moment balance of a four-bar linkage is implemented by using a Multi-Objective Genetic Algorithm (MOGA). During the time that an unbalanced linkage moves, it transmits shaking forces and moments to its surroundings. These transmitted forces and moments may cause some serious and undesirable problems such as vibration, noise, wear, and fatigue. In the current problem, the concepts of inertia counterweights and physical pendulum are utilized to complete balance of all mass effects (both linear and rotary, but excluding external loads), independent of input angular velocity. In this paper, Non-Dominated Genetic Algorithm (NSGA-II) is applied to minimize two objective functions subject to some different design constraints. The applied algorithm produced a set of feasible solutions called Pareto optimal solutions for the design problem. Finally, a fuzzy decision maker is applied to select the best solution among the obtained Pareto solutions based on design criteria. The results show that obtained solutions minimize the weights of applied counterweights and eliminate both shaking forces and moments transmitted to the ground, simultaneously.

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MULTI-OBJECTIVE CALIBRATION OF IBIS MODEL BY GENETIC ALGORITHM WITH PARAMETRIC SENSITIVITY ANALYSIS

Ciência e Natura ◽

10.5902/2179460x20095 ◽

2016 ◽

Vol 38 ◽

pp. 90

Author(s):

Amarísio Da Silva Araújo ◽

Haroldo De Campos Velho ◽

Lu Minjiao

Keyword(s):

Genetic Algorithm ◽

Good Description ◽

Synthetic Data ◽

Search Space ◽

Adjustment Process ◽

Nsga Ii ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Integrated Biosphere Simulator ◽

Ibis Model

Atmospheric circulation models combine different modules for a good description of the atmospheric dynamics. One of these modules is the representation of surface coverage, since the dynamics depends on the interaction between the atmosphere and the surface of the planet. However, these modules depend on a number of parameters that need to be adjusted. The parameter adjustment process is called model calibration. In this study, the IBIS (Integrated Biosphere Simulator) model is calibrated following a multi-objective strategy. The Pareto set, which embraces the non-dominated solutions in the search space of objective functions, is determined by a version of multi-objective genetic algorithm (NSGA-II). The model sensitivity to the parameters is evaluated by the Morris’ method. Synthetic data for calibration were obtained from the Tapajós National Forest (FloNa Tapajós), located near to the 67 km from Santarém-Cuiabá highway (2,51S, 54,58W).

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