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.     

Efficient loading and entrapment of tamoxifen in human serum albumin based nanoparticulate delivery system by a modified desolvation technique

Herein, the poorly water-soluble drug, Tamoxifen (Tmx), was loaded in the amphipathic matrix of human serum albumin (HSA) nanoparticles by a modified desolvation method. In order to enhance the drug loading (DL) and drug entrapment efficiency (DEE) (<2% and 10%, respectively), ultrasonication of Tmx-HSA mixture was performed prior to desolvation process. Tmx loading and entrapment efficiency were optimized by employment of the response surface methodology (RSM)-central composite design (CCD) of experiments. Under the optimum conditions of 1.59 mg Tmx/ml concentration, 7.76 pH and 5 h incubation of HSA-Tmx, the DL of 6.7% and DEE of 74% are achievable. Particles with the average size of 195 nm, zeta potential of −21 mV and polydispersity index of 0.09 were produced under these conditions. A more sustained Tmx release behavior was observed from polyethylene glycol (PEG) conjugated nanoparticles in comparison to the non-PEGylated ones. The short-term stability investigation showed no alteration in physicochemical properties of nanoparticles at 4 and 37 °C, but small increase in nanoparticles size was observed after three months of storage at room temperature. This is the first report for efficient production of a Tmx delivery system based on HSA nanoparticles.     

Effect of pulsed plasma nitriding on mechanical and tribological performance of Ck45 steel

We studied the mechanical properties and wear performance of AISI 1045 (Ck45) carbon steel under the influence of pulsed plasma nitriding. The treatments were performed at temperatures of 500 and 550 degrees C in N2:H2 gas ratios of 1:3 and 3:1 and the working pressure of 10 mbar for 1 to 4 hours. Samples were examined by X-ray diffraction, optical, electron and atomic force microscopy, microhardness tests, roughness measurements and wear tests. Nitride layers were mainly composed of epsilon-(Fe2-3N) or gamma'-(Fe4N) depending on the gas ratio and/or temperature and time. When the nitriding time is increased, the composition of the compound layer varies from monophase gamma'-(Fe4N) to the two phase of epsilon-(Fe2-3N) and gamma'-(Fe4N). The highest thickness and hardness of the layers were obtained at 550 degrees C in the N2:H2 gas ratios of 3:1 for 4 h. The topographical evolution and surface roughness of the samples showed that all the roughness parameters increase with increasing the temperature. The friction coefficient of all samples was higher than that of untreated material. Wear performance of all nitrided samples was significantly better than that of untreated material.     

Plasma Spray Deposition Enhancement of Titanium Nitride Layer Using Nitrogen-Contained Plasma Irradiation of Titanium Substrate as a Pre-spraying Method

Reactive plasma spray of TiN ceramic coating attracts much attention over the years because of its ability to deposit thick layers on various metal surfaces. However, some mechanical properties of the coating such as its hardness should be improved. In this study, initially a thin layer of titanium nitride was prepared on a titanium substrate during irradiation of titanium substrate by a thermal DC nitrogen-contained plasma jet. Then, during reactive plasma spraying, Ti particles were injected into plasma jet, converted to titanium nitride and huddled on to the substrate. This new hybrid method (primary plasma irradiation and post-reactive plasma spraying) for deposition of TiN coatings would combine the advantages of both plasma-enhanced chemical vapor deposition and reactive plasma spraying methods in part. It resulted in a thick and hard layer of titanium nitride film. Sample produced by this method was analyzed with x-ray diffraction confirming titanium nitride production. Vickers hardness was measured using optical microscopy which was around 1319 H_v300g. To study the cross section of the layer, optical microscopy and scanning electron microscopy (SEM) were used.     

Selective harmonic elimination method for wide range of modulation indexes in multilevel inverters using ICA

Selective harmonic elimination（SHE） in multilevel inverters is an intricate optimization problem that involves a set of nonlinear transcendental equations which have multiple local minima. A new advanced objective function with proper weighting is proposed and also its efficiency is compared with the objective function which is more similar to the proposed one. To enhance the ability of the SHE in eliminating high number of selected harmonics, at each level of the output voltage, one slot is created. The SHE problem is solved by imperialist competitive algorithm（ICA）. The conventional SHE methods cannot eliminate the selected harmonics and satisfy the fundamental component in some ranges of modulation indexes. So, to surmount the SHE defect, a DC-DC converter is applied. Theoretical results are substantiated by simulations and experimental results for a 9-level multilevel inverter. The obtained results illustrate that the proposed method successfully minimizes a large number of identified harmonics which consequences very low total harmonic distortion of output voltage.     

Rashba spin−orbit coupling effect on tunneling time in semiconductor spintronic junctions

Based on the transfer-matrix method and the group velocity concept, we investigate theoretically the spin tunneling time through single and double barriers of diluted magnetic semiconductor structures in the presence of the Rashba spin?orbit coupling (RSOC) effect. The calculation of transmission probability is based on an effective mass quantum-mechanical approach in the presence of an external magnetic field applied along the growth direction of the junction. The results show that the RSOC has great different influence on spin-dependent tunneling time of electrons with spin up and spin down in these structures. We also study the effect of zero-field conduction band offset on spin-dependent transmission properties. It is found that in the presence of RSOC and a positive zero-field conduction band offset as high as 10 meV, the results show a high degree of spin polarization and spin separation in the tunneling time in the considered system and this aspect may be utilized in designing new spin filter devices.     

Surface transformation of Ti-45Al-2Nb-2Mn-1B titanium aluminide by electron beam melting

Microstructure and phase evolution on the surface of Ti-45Al-2Nb-2Mn-1B (at.%) gamma based titanium aluminide was investigated by a series of electron beam melting with different beam energies and scanning speeds. X-Ray Diffraction (XRD), Glow Discharge Spectroscopy (GDS), Optical Microscopy (OM) and Scanning Electron Microscopy (SEM) were performed to characterize the phase modification and morphology after the EBM treatment.At beam energies of 250 W and scanning speed of 16 mm s⁻¹, the lamellar structure of Ti-45Al-2Nb-2Mn-1B transformed into a dendritic structure composed of initial α₂ (Ti₃Al) dendrites and an interdendritic phase of the γ (TiAl). While at higher energies of 350 W and lower beam speeds of 7 mm s⁻¹, mainly B2 and α₂ (Ti₃Al) phases with higher titanium formed on the surface.All Phase transformations increased the hardness of the surface to a maximum of 600 HV if compared to 330 HV for untreated material. Lower energies and higher speeds induced cracks in the surface layers, while higher energies and lower speeds produced hard surface layers without cracking.     

Multi-product selective newsvendor problem with service level constraints and market selection flexibility

We consider a multi-product multi-market newsvendor problem where the decision-maker could select some markets to serve. The considered problem involves the integration of procurement and market selection decisions. The products are procured from an external supplier. We assume that the realised demand for each product should be satisfied. In the case of shortages, the firm procures items at a higher cost. The paper considers the case where the selling prices, market entry costs, and product demand distributions are market dependent. Specifically, we discuss three cases of the Multi-Product Selective Newsvendor Problem: flexible market entry case, full market entry case and partial market entry case. The mathematical models of the above cases result in binary nonlinear programmes. We develop solution algorithms for solving the resulting combinatorial problems. Some managerial insights are provided.     

Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains

Data envelopment analysis (DEA) is a method for measuring performance of decision making units (DMUs). Conventional DEA models view DMUs as black boxes. Network DEA (NDEA) models have been developed to overcome this shortfall. This paper develops a new NDEA model based on modified enhanced Russell measure model. This paper measures performance of humanitarian supply chains (HSCs) by an NDEA model. Capabilities of the proposed model are addressed by theorems. However, in the real world, there might be stochastic data. This paper presents a stochastic version of the proposed NDEA model to measure the performance of HSCs. We analyse main properties of our model. We present a case study to demonstrate the applicability of the proposed model.