Flower pollination algorithm with pollinator attraction

The Flower Pollination Algorithm (FPA) is a highly efficient optimization algorithm that is inspired by the evolution process of flowering plants. In the present study, a modified version of FPA is proposed accounting for an additional feature of flower pollination in nature that is the so-called pollinator attraction. Pollinator attraction represents the natural tendency of flower species to evolve in order to attract pollinators by using their colour, shape and scent as well as nutritious rewards. To reflect this evolution mechanism, the proposed FPA variant with Pollinator Attraction (FPAPA) provides fitter flowers of the population with higher probabilities of achieving pollen transfer via biotic pollination than other flowers. FPAPA is tested against a set of 28 benchmark mathematical functions, defined in IEEE-CEC’13 for real-parameter single-objective optimization problems, as well as structural optimization problems. Numerical experiments show that the modified FPA represents a statistically significant improvement upon the original FPA and that it can outperform other state-of-the-art optimization algorithms offering better and more robust optimal solutions. Additional research is suggested to combine FPAPA with other modified and hybridized versions of FPA to further increase its performance in challenging optimization problems

Flower pollination algorithm parameters tuning

The flower pollination algorithm (FPA) is a highly efficient metaheuristic optimization algorithm that is inspired by the pollination process of flowering species. FPA is characterised by simplicity in its formulation and high computational performance. Previous studies on FPA assume fixed parameter values based on empirical observations or experimental comparisons of limited scale and scope. In this study, a comprehensive effort is made to identify appropriate values of the FPA parameters that maximize its computational performance. To serve this goal, a simple non-iterative, single-stage sampling tuning method is employed, oriented towards practical applications of FPA. The tuning method is applied to the set of 28 functions specified in IEEE-CEC'13 for real-parameter single-objective optimization problems. It is found that the optimal FPA parameters depend significantly on the objective functions, the problem dimensions and affordable computational cost. Furthermore, it is found that the FPA parameters that minimize mean prediction errors do not always offer the most robust predictions. At the end of this study, recommendations are made for setting the optimal FPA parameters as a function of problem dimensions and affordable computational cost. [Abstract copyright: © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.

Efficient optimum seismic design of reinforced concrete frames with nonlinear structural analysis procedures

Performance - based seismic design offers enhanced control of structural damage for different levels of earthquake hazard. Nevertheless, the number of studies dealing with the optimum performance - based seismic design of reinforced concrete frames is rather limited. This observation can be attributed to the need for nonlinear structural analysis procedures to calculate seismic demands. Nonlinear analysis of reinforced concrete frames is accompanied by high computational costs and require s a priori knowledge of steel reinforcement. To address this issue, previous studies on optimum performance-based seismic design of reinforced concrete frames use independent design variables to represent steel reinforcement in the optimization problem. This approach drives to a great number of design variables , which magnifies exponentially the search space undermining the ability of the optimization algorithms to reach the optimum solutions. This study presents a computationally efficient procedu re tailored to the optimum performance-based seismic design of reinforced concrete frames. The novel feature of the proposed approach is that it employs a deformation-based, iterative procedure for the design of steel reinforcement of reinforced concrete frames to meet their performance objectives given the cross-sectional dimensions of the structural me mbers. In this manner, only the cross-sectional dimensions of structural members need to be addressed by the optimization algorithms as independent design variables. The developed solution strategy is applied to the optimum seismic design of reinforced concrete frames using pushover and nonlinear response-history analysis and it is found that it outperforms previous solution approaches

Selection of earthquake ground motions for multiple objectives using genetic algorithms

Existing earthquake ground motion (GM) selection methods for the seismic assessment of structural systems focus on spectral compatibility in terms of either only central values or both central values and variability. In this way, important selection criteria related to the seismology of the region, local soil conditions, strong GM intensity and duration as well as the magnitude of scale factors are considered only indirectly by setting them as constraints in the pre-processing phase in the form of permissible ranges. In this study, a novel framework for the optimum selection of earthquake GMs is presented, where the aforementioned criteria are treated explicitly as selection objectives. The framework is based on the principles of multi-objective optimization that is addressed with the aid of the Weighted Sum Method, which supports decision making both in the pre-processing and post-processing phase of the GM selection procedure. The solution of the derived equivalent single-objective optimization problem is performed by the application of a mixed-integer Genetic Algorithm and the effects of its parameters on the efficiency of the selection procedure are investigated. Application of the proposed framework shows that it is able to track GM sets that not only provide excellent spectral matching but they are also able to simultaneously consider more explicitly a set of additional criteria

Surrogate-based optimum design of 3D reinforced concrete building frames to Eurocodes

The optimum structural design of real-world 3D concrete building frames to modern design standards is a complex and computationally expensive task. Hence, the use of surrogate-based optimization (SBO) methodologies must be investigated to reduce computational cost. The present study applies, for first time, a fully-fledged SBO algorithm to the optimum design of 3D concrete building frames. More particularly, the algorithm is applied to the minimum material cost design of a 4-storey and a 12-storey 3D RC building according to Eurocodes. It is found that the SBO algorithm can converge earlier than other well-established metaheuristic optimization algorithms reducing considerably the required computational effort. Nevertheless, it is likely to get trapped in local optima for large-scale RC frames. To overcome this drawback, a novel hybrid approach is also proposed herein that offers improved computational performance for large-scale concrete building frames

Optimum design of reinforced concrete retaining walls with the flower pollination algorithm

The flower pollination algorithm (FPA) is anefficient metaheuristicoptimizationalgorithm mimickingthe pollinationprocessof flowering species. In this study, FPA is applied, for first time, to the optimum design of reinforced concrete (RC) cantilever retaining walls. It is foundthat FPA offers important savings with respect to conventional design approachesand that it outperformsgenetic algorithm (GA)andthe particle swarm optimization (PSO) algorithm in this designproblem.Furthermore, parameter tuning reveals that the best FPA performance is achieved for switch probability values ranging between 0.4 and 0.7, a population size of 20 individualsand aLévy flightstep sizescale factor of 0.5. Finally, parametric optimum designs show that theoptimumcost of RC retaining walls increases rapidly with the wallheight and smoothly with the magnitude of surcharge loadin

Damage Analysis of Reinforced Concrete Structures with Substandard Detailing

The goal of this study is to investigate seismic behaviour of existing R/C buildings designed and constructed in accordance with standards that do not meet current seismic code requirements. In these structures, not only flexure, but also shear and bond-slip deformation mechanisms need to be considered, both separately and in combination. To serve this goal, a finite element model is developed for inelastic seismic analysis of complete planar R/C frames. The proposed finite element is able to capture gradual spread of inelastic flexural and shear deformations as well as their interaction in the end regions of R/C members. Additionally, it is capable of predicting shear failures caused by degradation of shear strength in the plastic hinges of R/C elements, as well as pullout failures caused by inadequate anchorage of the reinforcement in the joint regions. The finite element is fully implemented in the general inelastic finite element code IDARC2D and it is verified against experimental results involving individual column and plane frame specimens with nonductile detailing. It is shown that, in all cases, satisfactory correlation is established between the model predictions and the experimental evidence. Finally, parametric studies are conducted to illustrate the significance of each deformation mechanism on the seismic response of the specimens under investigation. It is concluded, that all deformation mechanisms, as well as their interaction, should be taken into consideration in order to predict reliably seismic damage of R/C structures with substandard detailing

P-n junction photocurrent modelling evaluation under optical and electrical excitation

Based upon the quasi-equilibrium approximation, the validity of p-n junction modelling, has been experimentally investigated under synchronous electrical and optical excitation of silicon photo-diodes. The devices had areas of 8.2 mm(2) and reverse bias saturation currents of the order of 10(-10) A. Their current-voltage (I-V) response was exploited experimentally both in the dark and under various illumination levels. The quoted values for the saturation current, the ideality factor, the series resistance and the reverse-bias photocurrent are investigated for the simulation of the I-V curves via the quasi-equilibrium model. In addition, the measured I-V data have been further analysed to estimate the produced photocurrent as a function of the applied bias (forward or reverse) under given illumination levels. Comparisons between the simulated curves and the experimental data allowed a detailed photocurrent modelling validation. The proposed approach could be useful towards studying other parameters of optically activated p-n junctions such as: the bias dependence of the minority carrier diffusion lengths and/or the generated rates of electron-hole pairs (EHP)

Exploring the impact of agricultural price and trade policy reform under transition in Albania

Agricultural policy decision making in economies undergoing transition to market is in need of empirical tools for assessing the impact of alternative policy options. The econometric means available for such exercise, however, are limited for lack of data and structural breaks in economic behaviour. Synthetic, partial equilibrium, multi-market models offer a potential alternative. Such models have been used extensively, even in advanced economies, to offer valuable insights on the impact of alternative policy options. This paper shows the use of such a partial equilibrium, multi-market, synthetic-type model as a tool for agricultural policy analysis in a country in transition. The model is applied for Albania, a predominantly agricultural country that, after a period of centralism and autarky, aims to re-join the international economic system. The model, albeit its many limitations, offers some useful insights on the impact of alternative options available for agricultural price and trade policy