Real-coded GAs Research Articles

Designing Optimum Locations and Properties of MTMD Systems

Reducing vibration of buildings during earthquake is of the primary concern to most structural engineers. Several methods have been proposed including the use of damper systems. This paper considers the optimization procedures of multi tuned mass damper (MTMD) systems. A number of researches have considered designing MTMD systems to reduce structural response during earthquake. However, most of the research considered only the properties of the MTMD, while the locations of MTMD are decided beforehand. This paper considers the optimization both the properties and location of MTMD in structures. The hybrid coded genetic algorithms (HCGAs) are used to optimize the dampers. The HCGAs are the optimization method that utilize binary coded GAs (BCGAs) and real coded GAs (RCGAs). The RCGAs are used to optimize the properties of MTMD, while the BCGAs are utilized to optimize the location of the dampers. Numerical examples are then carried out to see the ability of the proposed method in optimizing the locations and the properties of the dampers. Numerical examples are carried out to a three, ten-, and forty-story buildings. For the three- and ten-story buildings, the location of the MTMD is obtained at the top of the buildings, whereas for forty-story building the location of the dampers is depending on the mass ratio of the dampers. For 1% mass ratio, the locations of the dampers, are at the 39th and 40th floors, respectively. For 2% mass ratio, the dampers locations are obtained at the 38th and 40th floor, respectively; while for 4% mass ratio, the dampers locations are at 37th and 40th floors, respectively. Numerical simulations show the effectiveness of MTMD systems in reducing response of structures due to earthquake.

Adaptive evolutionary programming with p-best mutation strategy

Although initially conceived for evolving finite state machines, Evolutionary Programming (EP), in its present form, is largely used as a powerful real parameter optimizer. For function optimization, EP mainly relies on its mutation operators. Over past few years several mutation operators have been proposed to improve the performance of EP on a wide variety of numerical benchmarks. However, unlike real-coded GAs, there has been no fitness-induced bias in parent selection for mutation in EP. That means the i-th population member is selected deterministically for mutation and creation of the i-th offspring in each generation. In this article we propose a p-best mutation scheme for EP where any one from the p (p∈[1,2,…,μ], where μ denotes population size) top-ranked population-members (according to fitness values) is selected randomly for mutation. The scheme is invoked with 50% probability with each index in the current population, i.e. the i-th offspring can now be obtained either by mutating the i-th parent or by mutating a randomly selected individual from the p top-ranked vectors. The percentage of best members is made dynamic by decreasing p in from μ/2 to 1 with generations to favor explorative behavior at the early stages of search and exploitation during the later stages. We investigate the effectiveness of introducing controlled bias in parent selection in conjunction with an Adaptive Fast EP (AFEP), where the value of a strategy parameter is updated based on the previous records of successful mutations by the same parameter. Comparison with the recent and best-known versions of EP over 25 benchmark functions from the CEC (Congress on Evolutionary Computation) 2005 test-suite for real-parameter optimization and two other engineering optimization problems reflects the statistically validated superiority of the new scheme in terms of final accuracy, speed, and robustness. Comparison with AFEP without p-best mutation demonstrates the improvement of performance due to the proposed mutation scheme alone.

Saving MGG: Reducing Fitness Evaluations for Real-coded GA/MGG.

In this paper, we propose an extension of the Minimal Generation Gap (MGG) to reduce the number of fitness evaluation for the real-coded GAs (RCGA). When MGG is applied to actual engineering problems, for example applied to optimization of design parameters, the fitness calculating time is usually huge because MGG generates many children from one pair of parents and the fitness is calculated by repetitive simulation or analysis. The proposed method called Saving MGG reduces the number of fitness evaluation by estimating the promising degrees of children using individual distribution and fitness information of population, and selecting children based on the promising degree before evaluating the fitness. Experimental results show that RCGA with Saving MGG can provide large reducing effects on 20 or 30 dimensional Sphere functions, Rosenbrock functions, ill-scaled Rosenbrock functions, and Rastrigin function.