Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.

Genetical swarm optimization: a new hybrid evolutionary algorithm for electromagnetics

Abstract: This paper presents a new hybrid evolutionary algorithm combining Particle Swarm Optimization and Genetic Algorithms, called GSO (Genetical Swarm Optimization). GSO algorithm is essentially a population-based heuristic search technique which can be used to solve combinatorial optimization problems, modeled on the concept of natural selection but also based on cultural and social evolution. Numerical results and comparison of the different techniques are presented for an electromagnetic optimization problem.

Stochastic Spacecraft Thermal Design Optimization with Low Computational Cost

Abstract: This paper presents a strategy for a quick determination of the optimum configuration for radiators and solar absorbers in a spacecraft thermal design, to minimize heater power consumption and maximize temperature margins. It is particularly useful when applied to multimission platforms in which the thermal design is adapted for different orbits and operational modes. A two-step approach is adopted wherein a simplified thermal model is developed to search for the optimum radiator/solar absorber areas, and then the results are implemented in a detailed thermal model to verify the temperature distribution, thereby reducing computational time, a common drawback in complex engineering optimization problems. If necessary, small adjustments are then made in the radiator/solarabsorberconfiguration.Thesearchfortheoptimumdesignisaccomplishedusingarecentlyproposed global search metaheuristic, called generalized extremal optimization. Based on a model of natural evolution, it is easy to implement and has only one free parameter to adjust, making no use of derivatives. This paper presents the strategy as applied to the thermal design of the Brazilian Multimission Platform now under development. Nomenclature Ai = area of the radiator or solar absorber of node i, m 2 a = weighting factor for heater power consumption bl = weighting factor for temperature deviation for critical case l

Generalized extremal optimization for solving complex optimal design problems

Abstract: Recently, Boettcher and Percus [1] proposed a new optimization method, called Extremal Optimization (EO), inspired by a simplified model of natural selection developed to show the emergence of Self-Organized Criticality (SOC) in ecosystems [2]. Although having been successfully applied to hard problems in combinatorial optimization, a drawback of the EO is that for each new optimization problem assessed, a new way to define the fitness of the design variables has to be created [2]. Moreover, to our knowledge it has been applied so far to combinatorial problems with no implementation to continuous functions. In order to make the EO easily applicable to a broad class of design optimization problems, Sousa and Ramos [3,4] have proposed a generalization of the EO that was named the Generalized Extremal Optimization (GEO) method. It is of easy implementation, does not make use of derivatives and can be applied to unconstrained or constrained problems, non-convex or disjoint design spaces, with any combination of continuous, discrete or integer variables. It is a global search meta-heuristic, as the Genetic Algorithm (GA) and the Simulated Annealing (SA), but with the a priori advantage of having only one free parameter to adjust. Having been already tested on a set of test functions, commonly used to assess the performance of stochastic algorithms, the GEO proved to be competitive to the GA and the SA, or variations of these algorithms [3,4]. The GEO method was devised to be applied to complex optimization problems, such as the optimal design of a heat pipe (HP). This problem has difficulties such as an objective function that presents design variables with strong non-linear interactions, subject to multiple constraints, being considered unsuitable to be solved by traditional gradient based optimization methods [5]. To illustrate the efficacy of the GEO on dealing with such kind of problems, we used it to optimize a HP for a space application with the goal of minimizing the HP's total mass, given a desirable heat transfer rate and boundary conditions on the condenser. The HP uses a mesh type wick and is made of Stainless Steel. A total of 18 constraints were taken into account, which included operational, dimensional and structural ones. Temperature dependent fluid properties were considered and the calculations were done for steady state conditions, with three fluids being considered as working fluids: ethanol, methanol and ammonia. Several runs were performed under different values of heat transfer