An effortless hybrid method to solve economic load dispatch problem in power systems

This paper proposes a new approach and coding scheme for solving economic dispatch problems (ED) in power systems through an effortless hybrid method (EHM). This novel coding scheme can effectively prevent futile searching and also prevents obtaining infeasible solutions through the application of stochastic search methods, consequently dramatically improves search efficiency and solution quality. The dominant constraint of an economic dispatch problem is power balance. The operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones (POZ), network loss are considered for practical operation. Firstly, in the EHM procedure, the output of generator is obtained with a lambda iteration method and without considering POZ and later in a genetic based algorithm this constraint is satisfied. To demonstrate its efficiency, feasibility and fastness, the EHM algorithm was applied to solve constrained ED problems of power systems with 6 and 15 units. The simulation results obtained from the EHM were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time. (C) 2011 Elsevier Ltd. All rights reserved.

Multicast packing problem: abordagem multiobjetivo

This work presents a algorithmic study of Multicast Packing Problem considering a multiobjective approach. The first step realized was an extensive review about the problem. This review serverd as a reference point for the definition of the multiobjective mathematical model. Then, the instances used in the experimentation process were defined, this instances were created based on the main caracteristics from literature. Since both mathematical model and the instances were definined, then several algoritms were created. The algorithms were based on the classical approaches to multiobjective optimization: NSGA2 (3 versions), SPEA2 (3 versions). In addition, the GRASP procedures were adapted to work with multiples objectives, two vesions were created. These algorithms were composed by three recombination operators(C1, C2 e C3), two operator for build solution, a mutation operator and a local search procedure. Finally, a long experimentation process was performed. This process has three stages: the first consisted of adjusting the parameters; the second was perfomed to indentify the best version for each algorithm. After, the best versions for each algorithm were compared in order to identify the best algorithm among all. The algorithms were evaluated based on quality indicators and Hypervolume Multiplicative Epsilon

Use of genetic algorithms and solvation potential to study peptides structure

In this work, genetic algorithms concepts along with a rotamer library for proteins side chains and implicit solvation potential are used to optimize the tertiary structure of peptides. We starting from the known PDB structure of its backbone which is kept fixed while the side chains allowed adopting the conformations present in the rotamer library. It was used rotamer library independent of backbone and a implicit solvation potential. The structure of Mastoporan-X was predicted using several force fields with a growing complexity; we started it with a field where the only present interaction was Lennard-Jones. We added the Coulombian term and we considered the solvation effects through a term proportional to the solvent accessible area. This paper present good and interesting results obtained using the potential with solvation term and rotamer library. Hence, the algorithm (called YODA) presented here can be a good tool to the prediction problem. (c) 2007 Elsevier B.V. All rights reserved.

Fundamentos de otimização por inteligência de enxames: uma visão geral

Este artigo apresenta uma breve revisão de alguns dos mais recentes métodos bioinspirados baseados no comportamento de populações para o desenvolvimento de técnicas de solução de problemas. As metaheurísticas tratadas aqui correspondem às estratégias de otimização por colônia de formigas, otimização por enxame de partículas, algoritmo shuffled frog-leaping, coleta de alimentos por bactérias e colônia de abelhas. Os princípios biológicos que motivaram o desenvolvimento de cada uma dessas estratégias, assim como seus respectivos algoritmos computacionais, são introduzidos. Duas aplicações diferentes foram conduzidas para exemplificar o desempenho de tais algoritmos. A finalidade é enfatizar perspectivas de aplicação destas abordagens em diferentes problemas da área de engenharia.

On the solution of the extended linear complementarity problem

The extended linear complementarity problem (XLCP) has been introduced in a recent paper by Mangasarian and Pang. In the present research, minimization problems with simple bounds associated to this problem are defined. When the XLCP is solvable, their solutions are global minimizers of the associated problems. Sufficient conditions that guarantee that stationary points of the associated problems are solutions of the XLCP will be proved. These theoretical results support the conjecture that local methods for box constrained optimization applied to the associated problems could be efficient tools for solving the XLCP. (C) 1998 Elsevier B.V. All rights reserved.

Designing a modified Hopfield network to solve an Economic Dispatch problem with nonlinear cost function

Economic Dispatch (ED) problems have recently been solved by artificial neural networks approaches. In most of these dispatch models, the cost function must be linear or quadratic. Therefore, functions that have several minimum points represent a problem to the simulation since these approaches have not accepted nonlinear cost function. Another drawback pointed out in the literature is that some of these neural approaches fail to converge efficiently towards feasible equilibrium points. This paper discusses the application of a modified Hopfield architecture for solving ED problems defined by nonlinear cost function. The internal parameters of the neural network adopted here are computed using the valid-subspace technique, which guarantees convergence to equilibrium points that represent a solution for the ED problem. Simulation results and a comparative analysis involving a 3-bus test system are presented to illustrate efficiency of the proposed approach.

Efficient linear programming algorithm for the transmission network expansion planning problem

The transmission network planning problem is a non-linear integer mixed programming problem (NLIMP). Most of the algorithms used to solve this problem use a linear programming subroutine (LP) to solve LP problems resulting from planning algorithms. Sometimes the resolution of these LPs represents a major computational effort. The particularity of these LPs in the optimal solution is that only some inequality constraints are binding. This task transforms the LP into an equivalent problem with only one equality constraint (the power flow equation) and many inequality constraints, and uses a dual simplex algorithm and a relaxation strategy to solve the LPs. The optimisation process is started with only one equality constraint and, in each step, the most unfeasible constraint is added. The logic used is similar to a proposal for electric systems operation planning. The results show a higher performance of the algorithm when compared to primal simplex methods.

ACCELERATING ITERATIVE ALGORITHMS FOR SYMMETRICAL LINEAR COMPLEMENTARITY-PROBLEMS

We propose a method for accelerating iterative algorithms for solving symmetric linear complementarity problems. The method consists in performing a one-dimensional optimization in the direction generated by a splitting method even for non-descent directions. We give strong convergence proofs and present numerical experiments that justify using this acceleration.

Comparative studies on non-convex optimization methods for transmission network expansion planning

We have investigated and extensively tested three families of non-convex optimization approaches for solving the transmission network expansion planning problem: simulated annealing (SA), genetic algorithms (GA), and tabu search algorithms (TS). The paper compares the main features of the three approaches and presents an integrated view of these methodologies. A hybrid approach is then proposed which presents performances which are far better than the ones obtained with any of these approaches individually. Results obtained in tests performed with large scale real-life networks are summarized.

Neural approach for solving several types of optimization problems

Neural networks consist of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent neural net-works that can be used to solve several classes of optimization problems. More specifically, a modified Hopfield network is developed and its inter-nal parameters are computed explicitly using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points, which represent a solution of the problem considered. The problems that can be treated by the proposed approach include combinatorial optimiza-tion problems, dynamic programming problems, and nonlinear optimization problems.

An adaptation of the dual-affine interior point method for the surface flatness problem

This paper presents an adaptation of the dual-affine interior point method for the surface flatness problem. In order to determine how flat a surface is, one should find two parallel planes so that the surface is between them and they are as close together as possible. This problem is equivalent to the problem of solving inconsistent linear systems in terms of Tchebyshev's norm. An algorithm is proposed and results are presented and compared with others published in the literature. (C) 2006 Elsevier B.V. All rights reserved.

Analysis of heuristic algorithms for the transportation model in static and multistage planning in network expansion systems

The usefulness of the application of heuristic algorithms in the transportation model, first proposed by Garver, is analysed in relation to planning for the expansion of transmission systems. The formulation of the mathematical model and the solution techniques proposed in the specialised literature are analysed in detail. Starting with the constructive heuristic algorithm proposed by Garver, an extension is made to the problem of multistage planning for transmission systems. The quality of the solutions found by heuristic algorithms for the transportation model is analysed, as are applications in problems of planning transmission systems.

Genetic Algorithms (Binary and Real Codes) for the Optimisation of a Fermentation Process for Butanol Production

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An evolutionary algorithm for the one-dimensional cutting stock problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Fourth-order method for solving the Navier-Stokes equations in a constricting channel

A fourth-order numerical method for solving the Navier-Stokes equations in streamfunction/vorticity formulation on a two-dimensional non-uniform orthogonal grid has been tested on the fluid flow in a constricted symmetric channel. The family of grids is generated algebraically using a conformal transformation followed by a non-uniform stretching of the mesh cells in which the shape of the channel boundary can vary from a smooth constriction to one which one possesses a very sharp but smooth corner. The generality of the grids allows the use of long channels upstream and downstream as well as having a refined grid near the sharp corner. Derivatives in the governing equations are replaced by fourth-order central differences and the vorticity is eliminated, either before or after the discretization, to form a wide difference molecule for the streamfunction. Extra boundary conditions, necessary for wide-molecule methods, are supplied by a procedure proposed by Henshaw et al. The ensuing set of non-linear equations is solved using Newton iteration. Results have been obtained for Reynolds numbers up to 250 for three constrictions, the first being smooth, the second having a moderately sharp corner and the third with a very sharp corner. Estimates of the error incurred show that the results are very accurate and substantially better than those of the corresponding second-order method. The observed order of the method has been shown to be close to four, demonstrating that the method is genuinely fourth-order. © 1977 John Wiley & Sons, Ltd.