Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems

An efficient optimization algorithm called teaching–learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, ?-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.     

Variations and extension of the convex–concave procedure

We investigate the convex–concave procedure, a local heuristic that utilizes the tools of convex optimization to find local optima of difference of convex (DC) programming problems. The class of DC problems includes many difficult problems such as the traveling salesman problem. We extend the standard procedure in two major ways and describe several variations. First, we allow for the algorithm to be initialized without a feasible point. Second, we generalize the algorithm to include vector inequalities. We then present several examples to demonstrate these algorithms.     

A hybrid cellular automaton–bi-directional evolutionary optimization algorithm for topological optimization of crashworthiness

This article proposes a new algorithm for topological optimization under dynamic loading which combines cellular automata with bi-directional evolutionary structural optimization (BESO). The local rules of cellular automata are used to update the design variables, which avoids the difficulty of obtaining gradient information under nonlinear collision conditions. The intermediate-density design problem of hybrid cellular automata is solved using the BESO concept of 0–1 binary discrete variables. Some improvement strategies are also proposed for the hybrid algorithm to solve certain problems in nonlinear topological optimization, e.g. numerical oscillation. Some typical examples of crashworthiness problems are provided to illustrate the efficiency of the proposed method and its ability to find the final optimal solution. Finally, numerical results obtained using the proposed algorithms are compared with reference examples taken from the literature. The results show that the hybrid method is computationally efficient and stable.     

Comparison of trust-region-based and evolutionary methods for optimization of flow geometries †

Optimization has become increasingly important in computer-aided engineering, and applications of engineering optimization to design, control, operations, and planning already exist. Since in the context of numerical flow simulation no information on the gradient information of the objective function is available, or is very difficult to obtain, in such optimization cases it is advantageous to use an optimization technique which does not directly depend on the derivative. The aim of this study is to investigate two optimization techniques, the trust-region-based method and the evolutionary algorithm technique, and compare them quantitatively with respect to efficiency, quality, and working strategy. A tool based on free-form deformation (FFD) is employed for the variation of flow geometry. This simulation tool is the parallel multigrid flow solver FASTEST, which uses a fully conservative finite-volume method for solving the incompressible Navier–Stokes equations on a non-staggered cell-centred grid arrangement. The optimization tools are investigated by considering the optimization of the connection of two pipes with respect to the minimization of the pressure drop. This problem can be considered as a representative test case for a practical three-dimensional flow configuration.     

A highly accurate differential evolution–particle swarm optimization algorithm for the construction of initial value problem solvers

In this work a new evolutionary computation technique is introduced for the construction of initial value solvers based on Runge–Kutta (RK) pairs. The derivation of RK pairs corresponds to solving a nonlinear optimization problem with a multimodal objective function in a high dimensional search space; additional difficulty stems from the fact that only solutions with accuracy at least equal to machine epsilon are acceptable. The proposed approach involves hybridizing a Differential Evolution (DE) strategy with elements from Particle Swarm Optimization (PSO) in order to produce a method for solving optimization problems with high accuracy. The resulting methodology is applied to two different problems of RK pair derivation of orders 5 and 4 and compared with standard DE techniques. Numerical experiments show that the proposed hybrid DE-PSO satisfies the strict accuracy requirements imposed by the particular problem, while outperforming its rivals.     

Optimization of hybrid laser–TIG welding of 316LN stainless steel using genetic algorithm

Determination of optimum hybrid laser–TIG welding process variables for achieving the maximum depth of penetration (DOP) in type 316LN stainless steel has been carried out using a genetic algorithm (GA). Nd:YAG pulsed laser and the TIG heat source were coupled at the weld pool to carry out hybrid welding. Design of experiments approach was used to generate the experimental design matrix. Bead-on-plate welds were carried out based on the design matrix. The input variables considered were laser power, pulse frequency, pulse duration, and TIG current. The response variable considered was the DOP. Multiple-regression model was developed correlating the process variables with the DOP using the generated data. The regression model was used for evaluating the objective function in GA. GA-based model was developed and it produced a set of solutions. Tournament and roulette wheel selection methods were used during the execution of GA. It was found that both the selection methods identified similar welding process parameters for achieving the maximum DOP. Excellent agreement was observed between the target DOP and the DOP values obtained in the validation experiments during hybrid laser–TIG welding.     

Box–Behnken experimental design for preparation and optimization of the intranasal gels of selegiline hydrochloride

Selegiline hydrochloride (SL) is chosen as an adjunct for the control of clinical signs of Parkinsonian patients. The aim of the present work is to develop and optimize thermosensitive gels using Pluronic (F-127) for enhancing transport of SL into the brain through the nasal route. SL gels were prepared using a cold method and the Box–Behnken experimental design methodology. Drug (SL), gelling agent (F-127), and emulsifier (Propylene glycol, PG) were selected as independent variables, while the gelation temperature, gel strength, pH, gel content, and gel erosion were considered as dependent variables. For further understanding of the interaction between the various variables, contour plots and surface plots were also applied. Selected formulations, like S10 (contain 25?mg SL, 20?g F-127, and 1?g PG) and S14 (contain 50?mg SL, 18?g F-127 and 1?g PG), had a clear appearance in the sol form, with gelling temperature of the nasal gel ranging between 33 and 34, respectively. The gel strength of the formulations varied from 4.67 and 0.68?mm and the drug content was 100%. The pH of the formulations ranged between 6.71 and 7.11. Detachment force was acceptable (63.69–244.16 N/cm²) to provide prolonged adhesion. In vitro, drug release studies showed that the prepared formulations could release SL for up to 8?h. Permeation flux for the S10 gel was 0.0002?mg/min/cm². Results demonstrated that the potential use of SL gels can enhance the therapeutic effect of SL through the intranasal administration.     

Parameter identification of an elasto-plastic behaviour using artificial neural networks–genetic algorithm method

The simulation of the metal forming processes requires accurate constitutive models to describe the material behaviour at finite strain taking into account several conditions. The choice of a rheological model and the determination of its parameters should be made from a test that generates such conditions. The major difficulty encountered is that there is no experimental test satisfying all these criteria. The use of more than one test seems more and more essential, and it is utilized to characterize the rheological behaviour at operating conditions that correspond to metal forming applications. An Inverse analysis is then considered. Therefore, the difficulty lies within the long computing time taken when an optimization procedure is coupled with a finite element computation (FEC) to identify the material parameters. In order to solve the computing time problem, this paper proposes a hybrid identification method based on finite elements, neural network computations and genetic algorithm (GA) of an elasto-plastic behaviour model. The strategy suggested is then applied to identify the Karafillis and Boyce criterion and the Voce parameters model of the Stainless Steel AISI 304 using two tests (plane tensile test and bulge test with a circular die) at the same time.     

Tailoring the particle swarm optimization algorithm for?the?design of offshore oil production risers

In offshore oil production activities, risers are employed to connect the wellheads at the sea-bottom to a floating platform at the sea surface. The design of risers is a very important issue for the petroleum industry; many aspects are involved in the design of such structures, related to safety and cost savings, thus requiring the use of optimization tools. In this context, this work presents studies on the application of the Particle Swarm Optimization method (PSO) to the design of steel catenary risers in a lazy-wave configuration. The PSO method has shown good efficiency for some applications, but its performance is dependent on the values selected for the parameters of the algorithm. Therefore, this work describes some variants of the method, and presents results of several experiments performed to analyze the behavior of its parameters, trying to improve the performance of the method and tailor it for the application to the design of riser systems. The resulting method and its best set of parameters can then be taken as the default values in an implementation of the PSO method in the in-house OtimRiser computational tool, oriented to the design of risers, and also incorporating other optimization methods based on evolutionary concepts.     

Optimizing the static–dynamic performance of the body-in-white using a modified non-dominated sorting genetic algorithm coupled with grey relational analysis

This article presents a hybrid method combining a modified non-dominated sorting genetic algorithm (MNSGA-II) with grey relational analysis (GRA) to improve the static–dynamic performance of a body-in-white (BIW). First, an implicit parametric model of the BIW was built using SFE-CONCEPT software, and then the validity of the implicit parametric model was verified by physical testing. Eight shape design variables were defined for BIW beam structures based on the implicit parametric technology. Subsequently, MNSGA-II was used to determine the optimal combination of the design parameters that can improve the bending stiffness, torsion stiffness and low-order natural frequencies of the BIW without considerable increase in the mass. A set of non-dominated solutions was then obtained in the multi-objective optimization design. Finally, the grey entropy theory and GRA were applied to rank all non-dominated solutions from best to worst to determine the best trade-off solution. The comparison between the GRA and the technique for order of preference by similarity to ideal solution (TOPSIS) illustrated the reliability and rationality of GRA. Moreover, the effectiveness of the hybrid method was verified by the optimal results such that the bending stiffness, torsion stiffness, first order bending and first order torsion natural frequency were improved by 5.46%, 9.30%, 7.32% and 5.73%, respectively, with the mass of the BIW increasing by 1.30%.     

Influence of the preparation procedure and chitosan type on physicochemical properties and release behavior of alginate–chitosan microparticles

Background: The potential for use of chitosan-treated alginate microparticles as a vehicle for oral phenytoin delivery has not been thoroughly exploited. Aim: We studied the influence of preparation procedure and chitosan type on physicochemical properties and release behavior of alginate-chitosan microparticles. Method: The total number of 24 microparticles formulations prepared by varying contents of calcium gelling ions and varying contents and type of chitosan was examined. As an additional variable, two different hardening times (1 and 24 hours) were employed. Possible interactions of components, surface morphology of microparticles as well as release profile of phenytoin were studied. Results: Both series of formulations with regard to hardening times, irrespective of the chitosan type and/or concentration employed appeared to be highly loaded with the model drug (above 90%). The drug release studies showed that the kinetics of phenytoin cannot be straightforwardly predicted based on the molecular weight of chitosan alone. On the other hand, prolonging the hardening time from 1 to 24 hours had significantly improved phenytoin kinetics, and gave rise to a formulation with the liberation half-time of about 2.5 hours. Conclusion: This study showed that the latter formulation is eligible for further modifications aimed at improving the regularity of phenytoin absorption.     

Discrete size optimization of steel trusses using a refined big bang–big crunch algorithm

This article presents a methodology that provides a method for design optimization of steel truss structures based on a refined big bang–big crunch (BB-BC) algorithm. It is shown that a standard formulation of the BB-BC algorithm occasionally falls short of producing acceptable solutions to problems from discrete size optimum design of steel trusses. A reformulation of the algorithm is proposed and implemented for design optimization of various discrete truss structures according to American Institute of Steel Construction Allowable Stress Design (AISC-ASD) specifications. Furthermore, the performance of the proposed BB-BC algorithm is compared to its standard version as well as other well-known metaheuristic techniques. The numerical results confirm the efficiency of the proposed algorithm in practical design optimization of truss structures.     

Integrating grey sequencing with the genetic algorithm–immune algorithm to optimise touch panel cover glass polishing process parameter design

The touch panel cover glass is one of the important parts and components that determine touch panel quality. The quality requirement of touch panel cover glass emphasises the stability of glass thickness. As this factor directly influences the induction effect and touch of the touch panel, the parameter conditions for the cover glass polishing process have significant impact. This study integrated grey sequencing with the Genetic algorithm–Immune algorithm to optimise the parameter design for the touch panel cover glass polishing process. The experimental measurement value was the thickness value of the processed glass, and the uniformity of glass thickness after processing was discussed. The optimum processing combination influencing the process conditions is as follows: the ambient temperature is 22 (°C), the processing pressure is 0.04 (Mpa), the processing time is 30 (min), the machine speed is 70 (rpm), the polishing solution concentration is 1.4 (g/cm³), the central particle size of polishing powder is 1.4 (um) and the process capability C_pk is 1.75, which is better than the process capability of C_pk 1.41 of the response surface methodology and the process capability of C_pk 1.37 of the Taguchi experimental design.     

Application of genetic algorithm to the observer‐based controller design for neutral systems

Abstract

In this paper, the dynamic observer‐based controller design for a class of neutral systems with known and uncertain time delays is considered. Delay‐dependent and delay‐independent stabilizability criteria are proposed to guarantee the stability for the feedback control systems. Linear matrix inequality (LMI) and genetic algorithm (GA) are used to design the observer‐based control. Design procedure for the observer‐based control is provided. A numerical example is given to illustrate our results.     

Comparison of the effects of Mg–6Zn and titanium on intestinal tract in vivo

To evaluate the ability of Mg–6Zn to replace titanium nails in the reconstruction of the intestinal tract in general surgery, we compared the Mg–6Zn and titanium implants with respect to their effects on rat’s intestinal tract by biochemical, radiological, pathological and immunohistochemical methods. The results indicated that Mg–6Zn implants started to degrade at the third week and disintegrate at the fourth week. No bubbles appeared, which may be associated with intestinal absorption of the Mg–6Zn implants. Pathological analyses (containing liver, kidney and cecum tissues) and biochemical measurements, including serum magnesium, creatinine, blood urea nitrogen, glutamic–pyruvic–transaminase and glutamic–oxaloacetic–transaminase proved that degradation of Mg–6Zn did not harm the important organs, which is an improvement over titanium implants. Immunohistochemical results showed that Mg–6Zn could enhance the expression of transforming growth factor-β₁. Mg–6Zn reduced the expression of tumor necrosis factor at different stages. In general, our study demonstrates that the Mg–6Zn alloy had good biocompatibility in vivo and performed better than titanium at promoting healing and reducing inflammation. It may be a promising candidate for stapler pins in intestinal reconstruction.     

Comparison of the superelasticity of different nickel–titanium orthodontic archwires and the loss of their properties by heat treatment

The aim of this work is to describe and compare mechanical properties of eight widely used nickel–titanium orthodontic wires under uniform testing conditions and to determine the influence of the heat treatments on the loss of the superelasticity. Ten archwires from two batches from eight different manufacturers were evaluated. A three-point bending test was performed, in accordance with ISO 15841:2006, on 80 round nickel–titanium archwire segments of 0.016?inch. To obtain a load-deflection curve, the centre of each segment was deflected to 3.1?mm and then unloaded until force became zero. On the unloading curve, deflection at the end of the plateau and forces delivered at that point, and at 3, 2, 1 and 0.5?mm of deflection, were recorded. Plateau slopes were calculated from 3 and from 2?mm of deflection. Data obtained were statistically analysed to determine inter-brand, intra-brand and inter-batch differences (P?<?0.05). The results show that at 2?mm of deflection, maximum differential force exerted among brands [Nitinol SuperElastic (1.999N)—Sentalloy M (1.001?N)] was 0.998?N (102?gf). The Nitinol SuperElastic plateau slope (0.353?N/mm) was the only one that was statistically different from 2?mm of deflection, as compared with the other brand values (0.129–0.155?N/mm). Damon Optimal Force described the gentlest slope from 3?mm of deflection (0.230?N/mm) and one of the longest plateaus. Titanol and Orthonol showed the most notable intra-brand differences, whereas inter-batch variability was significant for Nitinol (Henry Schein), Euro Ni–Ti and Orthonol. Superelasticity degree and exerted forces differed significantly among brands. Superelasticity of Nitinol SuperElastic was not observed, while Damon Optimal Force and Proclinic Ni–Ti Superelástico (G&H) showed the most superelastic curves. Intra-brand and inter-batch differences were observed in some brands. In all cases, the heat treatment at 600?°C produces precipitation in the matrix. The precipitates are rich in titanium and this fact produce changes in the chemical composition of the matrix and the loss of the superelasticity. At 400?°C these precipitates are not produced and the forces delivered by the wires are very similar with wires untreated.     

Multi-objective teaching–learning-based optimization algorithm for reducing carbon emissions and operation time in turning operations

In addition to energy consumption, the use of cutting fluids, deposition of worn tools and certain other manufacturing activities can have environmental impacts. All these activities cause carbon emission directly or indirectly; therefore, carbon emission can be used as an environmental criterion for machining systems. In this article, a direct method is proposed to quantify the carbon emissions in turning operations. To determine the coefficients in the quantitative method, real experimental data were obtained and analysed in MATLAB. Moreover, a multi-objective teaching–learning-based optimization algorithm is proposed, and two objectives to minimize carbon emissions and operation time are considered simultaneously. Cutting parameters were optimized by the proposed algorithm. Finally, the analytic hierarchy process was used to determine the optimal solution, which was found to be more environmentally friendly than the cutting parameters determined by the design of experiments method.     

Comparison between hybridized algorithm of GA–SA and ABC,GA, DE and PSO for vertical-handover in heterogeneous wireless networks

Genetic algorithms (GAs) and simulated annealing (SA) have emerged as leading methods for search and optimization problems in heterogeneous wireless networks. In this paradigm, various access technologies need to be interconnected; thus, vertical handovers are necessary for seamless mobility. In this paper, the hybrid algorithm for real-time vertical handover using different objective functions has been presented to find the optimal network to connect with a good quality of service in accordance with the user’s preferences. As it is, the characteristics of the current mobile devices recommend using fast and efficient algorithms to provide solutions near to real-time. These constraints have moved us to develop intelligent algorithms that avoid slow and massive computations. This was to, specifically, solve two major problems in GA optimization, i.e. premature convergence and slow convergence rate, and the facilitation of simulated annealing in the merging populations phase of the search. The hybrid algorithm was expected to improve on the pure GA in two ways, i.e., improved solutions for a given number of evaluations, and more stability over many runs. This paper compares the formulation and results of four recent optimization algorithms: artificial bee colony (ABC), genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Moreover, a cost function is used to sustain the desired QoS during the transition between networks, which is measured in terms of the bandwidth, BER, ABR, SNR, and monetary cost. Simulation results indicated that choosing the SA rules would minimize the cost function and the GA–SA algorithm could decrease the number of unnecessary handovers, and thereby prevent the ‘Ping-Pong’ effect.     

Comparison of the cathodo- and γ-luminescence spectra of scintillation crystals

The spectra of pulsed cathodoluminescence (PCL) of CsI:Tl, YAG:Ge³⁺,Tb³⁺, and LFS-3 crystals excited by high-power (～10 MW) short-time (～2 ns) action of an electron beam with an average particle energy of ～150 keV have been compared to the spectra of steady-state luminescence of the same crystals under the action of γ-photons with an energy of 511 keV emitted from a radioactive ²²Na sample. It is established that the two spectra are identical, which allows the PCL method to be used for rapid analysis of scintillation crystals.     

A new hybrid teaching–learning particle swarm optimization algorithm for synthesis of linkages to generate path

This paper proposes a novel hybrid teaching–learning particle swarm optimization (HTLPSO) algorithm, which merges two established nature-inspired algorithms, namely, optimization based on teaching–learning (TLBO) and particle swarm optimization (PSO). The HTLPSO merges the best half of population obtained after the teacher phase in TLBO with the best half of the population obtained after PSO. The population so obtained is used subsequently in learner phase of TLBO. To validate the proposed algorithm, five constrained benchmark functions are considered to prove its robustness and efficiency. The proposed algorithm is applied to synthesize four-bar linkage for prescribed path. It is found that the HTLPSO performs better than other single nature-inspired algorithms for path synthesis problem in mechanism theory. Hence, HTLPSO may prove to be an important tool for mechanism design to follow the prescribed path.