The q-gradient is an extension of the classical gradient vector based on the concept of Jackson's derivative. Here we introduce a preliminary version of the q-gradient method for unconstrained global optimization. The main idea behind our approach is the use of the negative of the q-gradient of the objective function as the search direction. In this sense, the method here proposed is a generalization of the well-known steepest descent method. The use of Jackson's derivative has shown to be an effective mechanism for escaping from local minima. The q-gradient method is complemented with strategies to generate the parameter q and to compute the step length in a way that the search process gradually shifts from global in the beginning to almost local search in the end. For testing this new approach, we considered six commonly used test functions and compared our results with three Genetic Algorithms (GAs) considered effective in optimizing multidimensional unimodal and multimodal functions. For the multimodal test functions, the q-gradient method outperformed the GAs, reaching the minimum with a better accuracy and with less function evaluations.

This paper describes an application of the Generalized Extremal Optimization (GEO) algorithm to the inverse design of a spacecraft thermal control system. GEO is a recently proposed global search meta-heuristic [1], [2], [3] based on a model of natural evolution [4] , and specially devised to be used in complex optimization problems [5]. Easy to implement, GEO has only one free parameter to adjust, does not make use of derivatives and can be applied to constrained or unconstrained problems, non-convex or even disjoint design spaces, with any combination of continuous, discrete or integer variables. The application reported here concerns the optimum design of a simplified configuration of the Brazilian Multimission Platform (in Portuguese, Plataforma Multi-Missão, PMM) thermal control subsystem, comprising five radiators and one battery heater. The PMM is a multipurpose space platform to be used in different types of missions such as Earth observation, scientific or meteorological data collecting. The design procedure is tackled as a multi-objective optimization problem, considering two critical, operational hot and cold cases. The results indicate the existence of non-intuitive, new and more efficient design solutions.

No abstract

No abstract

No abstract

One issue the design team has to face in the process of building a new spacecraft, is to define its mechanical and electrical architecture. The choice of where to place the spacecraft´s electronic equipment is a complex task, since it involves simultaneously many factors, such as the spacecraft´s required position of center of mass, moments of inertia, equipment heat dissipation, integration and servicing issues, among others. Since this is a multidisciplinary task, the early positioning of the spacecraft´s equipment is usually done "manually" by a group of system engineers, heavily based on their experience. It is an interactive process that takes time and hence, as soon as a feasible design is found, it becomes the baseline. This precludes a broader exploration of the design space, which may lead to a suboptimal solution, or worse to a design that will have to be modified later. Recently, it has been shown the potential benefits of automating the process of spacecraft´s equipment layout using optimization techniques. In this paper, a prototype of an Excel ® based tool for multidisciplinary spacecraft equipment layout conception is described. Provided the geometric dimensions, mass and heat dissipation of the equipment, and the available positioning area, the tool can automatically generate many possible trade-off solutions for the layout. It allows the user to set specific equipment to specific areas of positioning, and different combinations of objective functions can be used to drive the design. The features of the tool are shown in a simplified three dimensional problem.

No abstract

In the present paper, a hybrid version of the Generalized Extremal Optimization (GEO) and Evolution Strategies (ES) algorithms [1], developed in order to conjugate the convergence properties of GEO with the self-tuning characteristics present in the ES, is applied to the estimation of the temperature distribution of the film cooling near the internal wall of a thruster. The temperature profile is determined through an inverse problem approach using the hybrid. The profile was obtained for steady-state conditions, were the external wall temperature along the thruster is considered as a known input. The Boltzmann’s equation parameters [2], which define the cooling film temperature profile, are the design variables. Results using simulated data showed that this approach was efficient in recuperating those parameters. The approach showed here can be used on the design of thrusters with lower wall temperatures, which is a desirable feature of such devices.