Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems

Optimization algorithms are important tools for the solution of combinatorial management problems. Nowadays, many of those problems are addressed by using evolutionary algorithms (EAs) that move toward a near-optimal solution by repetitive simulations. Sometimes, such extensive simulations are not possible or are costly and time-consuming. Thus, in this study a method based on artificial neural networks (ANN) is proposed to reduce the number of simulations required in EAs. Specifically, an ANN simulator is used to reduce the number of simulations by the main simulator. The ANN is trained and updated only for required areas in the decision space. Performance of the proposed method is examined by integrating it with the non-dominated sorting genetic algorithm (NSGAII) in multi-objective problems. In terms of density and optimality of the Pareto front, the hybrid NSGAII-ANN is able to extract the Pareto front with much less simulation time compared to the sole use of the NSGAII algorithm. The proposed NSGAII-ANN methodology was examined using three standard test problems (FON, KUR, and ZDT1) and one real-world problem. The latter addresses the operation of a reservoir with two objectives (meeting demand and flood control). Thus, based on this study, use of the NSGAII-ANN integrative algorithm in problems with time-consuming simulators reduces the required time for optimization up to 50 times. Results of the real-world problem, despite lower computational-time requirements, show a performance similar to that achieved in the aforementioned test problems.     

Modeling and Optimization of SE and SI of Copper Flotation via Hybrid GA–ANN

In the present paper, a new attitude has been proposed for optimization of the separation efficiency (SE) and the Gaudin’s selectivity index (SI) in a flotation process by Hybrid artificial neural network (ANN) and genetic algorithm (GA). The chemical reagent’s dosage (collector, frother and fuel oil), pH, solid percentage, feed rate, Cu, Mo, and Fe grades in the flotation feed were selected as input variables and the SE-Cu and SI-Mo and SI-Fe were selected as output ones. Multilayer NN with back propagation (BP) algorithm was trained by the standard Bayesian regulation algorithm in which the validation data set did not required to be apart from its training. This algorithm with four-layer was used to relate output and input variables. Employment of Hybrid GA–ANN method resulted in significant improvement on GA fitnesses, as SE-Cu = 88, SI-Mo = 4.47 and SI of Fe = 12.85 were achieved. The input parameters corresponding to the fitnesses were as follows: pH = 12.25; the grade of Cu = 0.55%, Mo = 0.04% and Fe = 5.53%; the collector, frother and fuel–oil concentrations being 16.55, 15.54 and 2.71 (g/ton), respectively; the solid percentage was 25.84% and feed rate was 38,380 ton/day. The best fitness of GA was obtained after 10 generations by MSE value of 2.23.     

Barium cobaltite nanoparticles: Sol-gel synthesis and characterization and their electrochemical hydrogen storage properties

The study of the effect of different chelating agents in the Pechini method on the morphology has been a promising strategy that can be used for practical tuning of the nanoparticle's morphology and hence the electrochemical hydrogen storage capacity. In the current study, the conventional Pechini sol-gel approach was used to prepare the Ba₂Co₉O₁₄ nanoparticles as a novel hydrogen storage material. The X-ray diffraction investigation approved the formation of Ba₂Co₉O₁₄ with a Hexagonal crystal structure for all of the synthesized samples. The scanning electron microscopy (SEM) revealed when citric acid was used as a chelating agent, nanoparticles with finer and more uniform morphology were obtained rather than other chelating sources. The transmission electron microscopy (TEM) showed in the presence of citric acid; the size of the synthesized nanoparticles was between 14 and 24 nm. According to the Diffuse Reflectance Spectroscopy (DRS) analysis, the calculated bandgap of synthesized nanoparticles was approximately 3.2 eV, which indicates that synthesized nanoparticles were semiconductors in essence. The electrochemical hydrogen adsorption/desorption results showed that the sample synthesized by the citric acid has an enhancement in electrochemical hydrogen storage of approximately 800 mAh/g after 15 cycles.     

Investigation of basic molecular gas structural effects on hydrodynamics and thermal behaviors of rarefied shear driven micro/nano flow using DSMC

In the present work, rarefied gas flow between two parallel moving plates maintained at the same uniform temperature is simulated using the direct simulation Monte Carlo (DSMC) method. Heat transfer and shear stress behavior in the micro/nano-Couette flow is studied and the effects of the important molecular structural parameters such as molecular diameter, mass, degrees of freedom and viscosity–temperature index on the macroscopic behavior of gases are investigated. Velocity, temperature, heat flux and shear stress in the domain are studied in details. Finally, a discussion on the role of the molecular structural parameters in the decrease or increase of amounts of hydrodynamics and thermal properties of the gas is presented.     

Green synthesis and characterization of RGO/Cu nanocomposites as photocatalytic degradation of organic pollutants in waste-water

Recently, nanocomposite photocatalysts based on semiconductors have attracted much attention due to their suitable bandgap. Combination of tow of several semiconductors can slow down the electron-hole recombination. In this regard, we have depicted an eco-friendly and green fabrication technique to synthesize RGO/Cu nanocomposite by the reduction of graphene oxide and Cu²⁺ ion utilizing spearmint extract as a reductant and capping agent. The sample was identified by FTIR, XRD, FESEM, EDS, HRTEM, and CV. The results of photocatalytic performance revealed that RGO/Cu is an efficient catalyst for degrading organic pollutants. This compound can eliminate Rhodamine B (RhB) and Methylene blue (MB) 91.0% and 72.0%, respectively.     

A parsimonious SVM model selection criterion for classification of real-world data sets via an adaptive population-based algorithm

This paper proposes and optimizes a two-term cost function consisting of a sparseness term and a generalized v-fold cross-validation term by a new adaptive particle swarm optimization (APSO). APSO updates its parameters adaptively based on a dynamic feedback from the success rate of the each particle’s personal best. Since the proposed cost function is based on the choosing fewer numbers of support vectors, the complexity of SVM model is decreased while the accuracy remains in an acceptable range. Therefore, the testing time decreases and makes SVM more applicable for practical applications in real data sets. A comparative study on data sets of UCI database is performed between the proposed cost function and conventional cost function to demonstrate the effectiveness of the proposed cost function.

     

Automatic navigation of mobile robots in unknown environments

Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32 % of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work.     

Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production

In this study, various Artificial Neural Networks (ANNs) were developed to estimate the production yield of greenhouse basil in Iran. For this purpose, the data collected by random method from 26 greenhouses in the region during four periods of plant cultivation in 2009–2010. The total input energy and energy ratio for basil production were 14,308,998 MJ ha^?1 and 0.02, respectively. The developed ANN was a multilayer perceptron (MLP) with seven neurons in the input layer, one, two and three hidden layer(s) of various numbers of neurons and one neuron (basil yield) in the output layer. The input energies were human labor, diesel fuel, chemical fertilizers, farm yard manure, chemicals, electricity and transportation. Results showed, the ANN model having 7-20-20-1 topology can predict the yield value with higher accuracy. So, this two hidden layer topology was selected as the best model for estimating basil production of regional greenhouses with similar conditions. For the optimal model, the values of the models outputs correlated well with actual outputs, with coefficient of determination (R²) of 0.976. For this configuration, RMSE and MAE values were 0.046 and 0.035, respectively. Sensitivity analysis revealed that chemical fertilizers are the most significant parameter in the basil production.