A specialized genetic algorithm to solve the short term transmission network expansion planning

In this paper, the short term transmission network expansion planning (STTNEP) is solved through a specialized genetic algorithm (SGA). A complete AC model of the transmission network is used, which permits the formulation of an integrated power system transmission network expansion planning problem (real and reactive power planning). The characteristics of the proposed SGA to solve the STTNEP problem are detailed and an interior point method is employed to solve nonlinear programming problems during the solution steps of the SGA. Results of tests carried out with two electrical energy systems show the capabilities of the SGA and also the viability of using the AC model to solve the STTNEP problem. © 2009 IEEE.

Specialized genetic algorithm for transmission network expansion planning considering reliability

This paper presents a methodology to solve the transmission network expansion planning problem (TNEP) considering reliability and uncertainty in the demand. The proposed methodology provides an optimal expansion plan that allows the power system to operate adequately with an acceptable level of reliability and in an enviroment with uncertainness. The reliability criterion limits the expected value of the reliability index (LOLE - Loss Of Load Expectation) of the expanded system. The reliability is evaluated for the transmission system using an analytical technique based in enumeration. The mathematical model is solved, in a efficient way, using a specialized genetic algorithm of Chu-Beasley modified. Detailed results from an illustrative example are presented and discussed. © 2009 IEEE.

Location and contract pricing of distributed generation using a genetic algorithm

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.

Combining Legendre's Polynomials and Genetic Algorithm in the Solution of Nonlinear Initial-Value Problems

This work develops a method for solving ordinary differential equations, that is, initial-value problems, with solutions approximated by using Legendre's polynomials. An iterative procedure for the adjustment of the polynomial coefficients is developed, based on the genetic algorithm. This procedure is applied to several examples providing comparisons between its results and the best polynomial fitting when numerical solutions by the traditional Runge-Kutta or Adams methods are available. The resulting algorithm provides reliable solutions even if the numerical solutions are not available, that is, when the mass matrix is singular or the equation produces unstable running processes.

A Constructive Genetic Algorithm for permutation flowshop scheduling

The general flowshop scheduling problem is a production problem where a set of n jobs have to be processed with identical flow pattern on in machines. In permutation flowshops the sequence of jobs is the same on all machines. A significant research effort has been devoted for sequencing jobs in a flowshop minimizing the makespan. This paper describes the application of a Constructive Genetic Algorithm (CGA) to makespan minimization on flowshop scheduling. The CGA was proposed recently as an alternative to traditional GA approaches, particularly, for evaluating schemata directly. The population initially formed only by schemata, evolves controlled by recombination to a population of well-adapted structures (schemata instantiation). The CGA implemented is based on the NEH classic heuristic and a local search heuristic used to define the fitness functions. The parameters of the CGA are calibrated using a Design of Experiments (DOE) approach. The computational results are compared against some other successful algorithms from the literature on Taillard`s well-known standard benchmark. The computational experience shows that this innovative CGA approach provides competitive results for flowshop scheduling; problems. (C) 2007 Elsevier Ltd. All rights reserved.

Evaluation of Adaptive Genetic Algorithm to Environmental Modeling of Peponapis and Cucurbita

This paper presents a free software tool that supports the next-generation Mobile Communications, through the automatic generation of models of components and electronic devices based on neural networks. This tool enables the creation, training, validation and simulation of the model directly from measurements made on devices of interest, using an interface totally oriented to non-experts in neural models. The resulting model can be exported automatically to a traditional circuit simulator to test different scenarios.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies. [DOI:10.1115/1.4002740]

Jointly multi-user detection and channel estimation with genetic algorithm

This work aims at proposing the use of the evolutionary computation methodology in order to jointly solve the multiuser channel estimation (MuChE) and detection problems at its maximum-likelihood, both related to the direct sequence code division multiple access (DS/CDMA). The effectiveness of the proposed heuristic approach is proven by comparing performance and complexity merit figures with that obtained by traditional methods found in literature. Simulation results considering genetic algorithm (GA) applied to multipath, DS/CDMA and MuChE and multi-user detection (MuD) show that the proposed genetic algorithm multi-user channel estimation (GAMuChE) yields a normalized mean square error estimation (nMSE) inferior to 11%, under slowly varying multipath fading channels, large range of Doppler frequencies and medium system load, it exhibits lower complexity when compared to both maximum likelihood multi-user channel estimation (MLMuChE) and gradient descent method (GrdDsc). A near-optimum multi-user detector (MuD) based on the genetic algorithm (GAMuD), also proposed in this work, provides a significant reduction in the computational complexity when compared to the optimum multi-user detector (OMuD). In addition, the complexity of the GAMuChE and GAMuD algorithms were (jointly) analyzed in terms of number of operations necessary to reach the convergence, and compared to other jointly MuChE and MuD strategies. The joint GAMuChE-GAMuD scheme can be regarded as a promising alternative for implementing third-generation (3G) and fourth-generation (4G) wireless systems in the near future. Copyright (C) 2010 John Wiley & Sons, Ltd.

Minimum-order stable recursive filter design via the genetic algorithm approach

In this paper, the minimum-order stable recursive filter design problem is proposed and investigated. This problem is playing an important role in pipeline implementation sin signal processing. Here, the existence of a high-order stable recursive filter is proved theoretically, in which the upper bound for the highest order of stable filters is given. Then the minimum-order stable linear predictor is obtained via solving an optimization problem. In this paper, the popular genetic algorithm approach is adopted since it is a heuristic probabilistic optimization technique and has been widely used in engineering designs. Finally, an illustrative example is sued to show the effectiveness of the proposed algorithm.

Simulated-annealing-based genetic algorithm for modeling the optical constants of solids

We propose a simulated-annealing-based genetic algorithm for solving model parameter estimation problems. The algorithm incorporates advantages of both genetic algorithms and simulated annealing. Tests on computer-generated synthetic data that closely resemble optical constants of a metal were performed to compare the efficiency of plain genetic algorithms against the simulated-annealing-based genetic algorithms. These tests assess the ability of the algorithms to and the global minimum and the accuracy of values obtained for model parameters. Finally, the algorithm with the best performance is used to fit the model dielectric function to data for platinum and aluminum. (C) 1997 Optical Society of America.

A genetic algorithm/method of moments approach to the optimization of an RF coil for MRI applications - Theoretical considerations - Abstract

A Combined Genetic Algorithm and Method of Moments design methods is presented for the design of unusual near-field antennas for use in Magnetic Resonance Imaging systems. The method is successfully applied to the design of an asymmetric coil structure for use at 190MHz and demonstrates excellent radiofrequency field homogeneity.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

Optimal dispatch with reactive power compensation by Genetic Algorithm

This paper presents a Unit Commitment model with reactive power compensation that has been solved by Genetic Algorithm (GA) optimization techniques. The GA has been developed a computational tools programmed/coded in MATLAB. The main objective is to find the best generations scheduling whose active power losses are minimal and the reactive power to be compensated, subjected to the power system technical constraints. Those are: full AC power flow equations, active and reactive power generation constraints. All constraints that have been represented in the objective function are weighted with a penalty factors. The IEEE 14-bus system has been used as test case to demonstrate the effectiveness of the proposed algorithm. Results and conclusions are dully drawn.

Ancillary services dispatch using linear programming and genetic algorithm approaches

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.