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