With the increasing availability of computational power, optimization Is becoming a credible and viable option when designing complex multidisciplinary systems. Computational optimization generally involves three dist...
详细信息
With the increasing availability of computational power, optimization Is becoming a credible and viable option when designing complex multidisciplinary systems. Computational optimization generally involves three distinct phases: 1) model the physical system in terms of design parameters and design metrics, 2) form an aggregate objective function in terms of the design metrics, and 3) minimize the aggregate objective function using an optimization code. Robust analytical and computational tools are available to perform the first and third phases. The analytical tools available for constructing the objective function in phase two are remarkably simplistic and generally involve difficult-to obtain weights. Because the optimum solution is only as effective as the aggregate objective function, any deficiency in the formation of the latter significantly impacts the ultimate outcome. The multiobjective design optimization process is examined from the perspective of constructing objective functions. We expose the shortcomings of weight-based methods using analytical and numerical means. Through analytical, graphical, and computational means, we show how the physical programming approach entirely circumvents the reliance on weight, thereby resulting in a new method of practical and general applicability.
An effective optimal spinning reserve allocation (OSRA) method is proposed in this paper using Optimal Fouler Flow (OPF). It enables optimal allocation of spinning reserve and load curtailment incorporating full AC ne...
详细信息
An effective optimal spinning reserve allocation (OSRA) method is proposed in this paper using Optimal Fouler Flow (OPF). It enables optimal allocation of spinning reserve and load curtailment incorporating full AC network constraints and dynamic restriction on generation such as ramp-rate constraints. A Primal-Dual Interior Point (PDIP) mettled, which can efficiently handle both equality constraints and inequality constraints, is employed to solve the formulated dynamic OPF problem. In this model, spinning reserve and load curtailment constraints impose an interdependency between the generation output of units which usually are separable in conventional Newton OPF. A decomposition algorithm is therefore derived to handle the interdependency so that the constraint matrix of generation and that of network can be handled separately by slightly changing the entries of Hessian matrix. Therefore, the proposed method is not only still able to utilize the elegant super sparsity technique of Newton method, but also eliminates its ineffective binding active set determination procedure. Furthermore, the influences of spinning reserve on spot price (SP) are also discussed. A shift effect is observed.
The development is described of a balanced field length trajectory optimization approach for a multi-engine helicopter sustaining a single engine failure. Whereas mast studies typically focus on a single flight phase,...
详细信息
The development is described of a balanced field length trajectory optimization approach for a multi-engine helicopter sustaining a single engine failure. Whereas mast studies typically focus on a single flight phase, this study is based on a multiphase formulation, in which the rejected takeoff and continued takeoff trajectories are optimized simultaneously, subject to a field length balancing constraint. The advantage of this approach is that, for any given engine failure time, it allows the Right phase where all engines are still operating to be optimized in such a way that the solution represents the best possible compromise between the conflicting requirements set by the rejected takeoff and continued takeoff flight phases. In addition to balanced field length calculations, the optimization of unbalanced rejected takeoff has been addressed. Combined considerations of balanced and unbalanced rejected takeoff give insight in the choice of the critical decision point. The most important result of the overall optimization process is the optimal all-engines-operating takeoff Right path up to the critical decision point. The usefulness of the proposed multiphase optimization approach is demonstrated in a numerical example involving a point-mass model of the UH-60A twin-engine helicopter.
The focus of this paper is to introduce a flexible identification strategy for a general class of models. We use DABNet models, which are composed of a linear state space system whose states, de- coupled by input, are...
详细信息
The focus of this paper is to introduce a flexible identification strategy for a general class of models. We use DABNet models, which are composed of a linear state space system whose states, de- coupled by input, are mapped by a neural network. The linear state space matrices can be represented as loosely coupled first and second order sections. A set of conditions will be analyzed for this kind of model that allows an identification process to be as flexible as possible. By flexible we mean a set of features that would be desirable to find in an identification approach: constraint handling and optimal-input design.
A new method for joint optimization of unit commitment and short-term bulk power trade is presented. It includes models representing a pluralistic market scenario including decentral trading and power pools. The utili...
详细信息
A new method for joint optimization of unit commitment and short-term bulk power trade is presented. It includes models representing a pluralistic market scenario including decentral trading and power pools. The utility operating most of the Austrian generation system with 90% hydro generation power has tested this method. These applicability tests are summarized here.
In chemical manufacturing processes, optimization problems with differential-algebraic constraints are frequently encountered. In general, these problems are difficult to solve and solution approaches are usually base...
详细信息
In chemical manufacturing processes, optimization problems with differential-algebraic constraints are frequently encountered. In general, these problems are difficult to solve and solution approaches are usually based on discretization schemes. This paper proposes an alternative method to determine optimal setpoint trajectories for a class of dynamic systems with differential-algebraic constraints. The method exploits di ff erential flatness to explicitly eliminate the differential state equations. The resulting optimization problem is an algebraic, nonlinearly constrained optimization problem, and can be solved by nonlinear programming. The proposed approach has the potential to reduce the amount of on-line computation required to solve a specific class of these problems in real-time.
This paper studies various strategies in constrained simulated annealing (CSA), a global optimization algorithm that achieves asymptotic convergence to constrained global minima (CGM) with probability one for solving ...
详细信息
This paper studies various strategies in constrained simulated annealing (CSA), a global optimization algorithm that achieves asymptotic convergence to constrained global minima (CGM) with probability one for solving discrete constrained nonlinear programming problems (NLPs). The algorithm is based on the necessary and sufficient condition for discrete constrained local minima (CLM) in the theory of discrete Lagrange multipliers and its extensions to continuous and mixed-integer constrained NLPs. The strategies studied include adaptive neighborhoods, distributions to control sampling, acceptance probabilities, and cooling schedules. We report much better solutions than the best-known solutions in the literature on two sets of continuous benchmarks and their discretized versions.
This article focuses on the multiobjective nonconvex nonlinear programming problem. The following interactive fuzzy satisficing method is proposed using the floating-point genetic algorithm. The fuzzy goal of the deci...
详细信息
This article focuses on the multiobjective nonconvex nonlinear programming problem. The following interactive fuzzy satisficing method is proposed using the floating-point genetic algorithm. The fuzzy goal of the decision-maker for each objective function is specified by the membership function. The Pareto optimal solution is derived, which is close to the reference membership value set by the decision-maker, in the sense of the augmented min-max criterion. If the decision-maker is not satisfied with the solution, the reference membership value is interactively updated to derive the satisficing solution for the decision-maker from the set of Pareto optimal solutions. In the derivation of the Pareto optimal solution for the augmented min-max problem, GENOCOP III proposed by Michalewicz and colleagues is not used. Instead, a more efficient method is proposed, where the improved GENOCOP III is applied to cope with the problem in GENOCOP III, by introducing the efficient search of the initial feasible solution, and the search of the feasible solution by bisection method. The validity of the proposed method is shown through numerical examples.
Some Optimal Control problems can be reduce to problems of nonlinear Progran1ming. Methods of penalty functions are widely used in nonlinear programming. Theorems of the existence of exact penalty parameters for solvi...
详细信息
Some Optimal Control problems can be reduce to problems of nonlinear Progran1ming. Methods of penalty functions are widely used in nonlinear programming. Theorems of the existence of exact penalty parameters for solving of the problems of nonlinear programming by the method of exact penalty functions are proved. The knowledge of any exact penalty parameter permits at once to decide the given problem in the presence of an algorithm of unconditional minimization of a nonsmooth function.
In this paper we have represented an information-theoretic algorithm of solving general non-linear constrained optimization problems on the basis of Maximum-entropy and minimum Cross-entropy principles. We have also e...
详细信息
In this paper we have represented an information-theoretic algorithm of solving general non-linear constrained optimization problems on the basis of Maximum-entropy and minimum Cross-entropy principles. We have also explained how the technique can be interestingly applied to solve linear programming problem.
暂无评论