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The use of three non‐linear mathematical programming techniques for the optimization of structural design problems is discussed. The methods — sequential linear programming, the feasible direction method and the sequential unconstrained minimization technique — are applied to a portal frame problem to enable a study of their convergence efficiency to be studied. These methods are used for both the sizing of the structural members and determining the optimum roof pitch. The sequential linear programming method is shown to be particularly efficient for application to structural design problems. Some comments on the development of computer software for structural optimization are also given.

This paper analytically models and numerically investigates two operating levers, namely optimization of product price and optimization of product quality in the context of a manufacturer that sells the products directly in the marketplace. The study attempts to identify how optimizing product quality and product price can fulfill a manufacturer's economic aims such as maximization of the manufacturer's profit and market demand. Anchored to the extant literature, the demand is modeled as a parametric joint multiplicative function of price and quality. Further, price is modeled as a function of product quality.

First, the authors evolve the analytical expression for the manufacturer's profit. Thereafter, following the mathematical principles of unconstrained optimization, the authors arrive at the conditions for optimal product quality and product price. The authors further perform numerical experiments to understand the behavior of economic dimensions such as profit and demand with respect to sensitivities associated with cost, quality and price.

The authors find that under product quality optimization, the optimal product quality is a unique solution in that a highest possible theoretical manufacturer's profit is obtained. However, in the case of product price optimization, the optimal product price is non-unique and is a function of product quality. The authors further find that in the context of functional quality-level expectations, product quality optimization as an operating lever gives a better dividend. However, in the case of higher product quality expectations, product price optimization performs better than product quality optimization. Further, several novel findings are also obtained from numerical experimentations.

The findings of the authors' study have implications for types of industries characterized by relatively low as well as relatively high competitive intensity. Further, as opposed to several extant studies that have often carried out joint optimization of quality and price, the authors' study is one of the first to study the impact of product price and product quality on the manufacturer's economic objective in a disparate and focused manner, thus capturing individual effects.

The design of optimum pipe and duct networks with available procedures is difficult, if not impossible. A more efficient procedure that will automatically produce the optimum design is required. Such a procedure is presented in this article. The design is formulated as a constrained nonlinear optimization problem. This problem is solved using a unique numerical optimization algorithm. The solution entails the calculation of the cross sectional dimensions of the ducts and pipes so that the life cycle cost of the network is minimized. The topology equations are derived using graph theory thereby allowing complex networks with loops to be designed numerically. A duct network consisting of a fan and 35 duct sections is designed according to certain specifications. Using the proposed procedure optimum designs were obtained within seconds on a 33 MHz 486 micro‐computer. The procedure was further applied to the optimization of a coal pipeline. It is shown that the optimized solution will cost 14% ($8 million) less than the previous design with conventional design techniques.

The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.

Utilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.

Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.

The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.

To study constrained or unconstrained global optimization problems in a cube of R^d where d is a positive integer.

α‐dense curves are initially used to transform this problem into a global optimization problem of a single variable. The optimization of the one variable is then treated by means of the Legendre‐Fenchel Transform. This discrete convex envelope of the one variable function obtained previously, can then be computed.

Global optimization problems of this nature have already been extensively studied by the authors. In this paper they have coupled the Alienor method with Legendre‐Fenchel Tranform to compute a discrete convex envelope of the function to minimize. A fast algorithm was successfully used to do this.

This approach to global optimization is based on α‐dense curves and numerical tests performed on a Pentium IV (1,700 MHz) computer used with Mathematica 4 software.

Gives the solutions illustrated in the numerous examples provided that show the practicality of the methodology.

A new approach based on extensive research into global optimization via α‐dense curves.

The purpose of this paper is to formulate the reliability optimization problem in stochastic and interval domain and also to solve the same under different stochastic set up.

Stochastic programming technique has been used to convert the chance constraints into deterministic form and the corresponding problem is transformed to mixed-integer constrained optimization problem with interval objective. Then the reduced problem has been converted to unconstrained optimization problem with interval objective by Big-M penalty technique. The resulting problem has been solved by advanced real coded genetic algorithm with interval fitness, tournament selection, intermediate crossover and one-neighbourhood mutation.

A new optimization technique has been developed in stochastic domain and the concept of interval valued parameters has been integrated with the stochastic setup so as to increase the applicability of the resultant solution to the interval valued nonlinear optimization problems.

The concept of probability distribution with interval valued parameters has been introduced. This concept will motivate the researchers to carry out the research in this new direction.

The application of genetic algorithm is extended to solve the reliability optimization problem in stochastic and interval domain.

The purpose of this paper is to define the process of analog circuit optimization on the basis of the control theory application. This approach produces many different strategies of optimization and determines the problem of searching of the best strategy in sense of minimal computer time. The determining of the best strategy of optimization and a searching of possible structure of this strategy with a minimal computer time is a principal aim of this work.

Different kinds of strategies for circuit optimization have been evaluated from the point of view of operations’ number. The generalized methodology for the optimization of analog circuit was formulated by means of the optimum control theory. The main equations for this methodology were elaborated. These equations include the special control functions that are introduced artificially. This approach generalizes the problem and generates an infinite number of different strategies of optimization. A problem of construction of the best algorithm of optimization is defined as a typical problem of the control theory. Numerical results show the possibility of application of this approach for optimization of electronic circuits and demonstrate the efficiency and perspective of the proposed methodology.

Examples show that the better optimization strategies that are appeared in limits of developed approach have a significant time gain with respect to the traditional strategy. The time gain increases when the size and the complexity of the optimized circuit are increasing. An additional acceleration effect was used to improve the properties of presented optimization process.

The obtained results show the perspectives of new approach for circuit optimization. A large set of various strategies of circuit optimization serves as a basis for searching the better strategies with a minimum computer time. The gain in processor time for the best strategy reaches till several thousands in comparison with traditional approach.

Optimization techniques can be used to design geometrically complex components with a wide variety of optimization criteria. However, such components have been very difficult and costly to produce. Layered fabrication technologies such as electron beam melting (EBM) open up new possibilities though. This paper seeks to investigate the integration of structural optimization and direct metal fabrication process.

Mesh structures were designed, and optimization problems were defined to improve structural performance. Finite element analysis code in conjunction with nonlinear optimization routines were used in MATLAB. Element data were extracted from an STL‐file, and output structures from the optimization routine were manufactured using an EBM machine. Original and optimized structures were tested and compared.

There were discrepancies between the performance of the theoretical structures and the physical EBM structures due to the layered fabrication approach. A scaling factor was developed to account for the effect of layering on the material properties.

Structural optimization can be used to improve the performance of a design, and direct fabrication technologies can be used to realise these structures. However, designers must realize that fabricated structures are not identical to idealized CAD structures, hence material properties much be adjusted accordingly.

Integration of structural optimization and direct metal fabrication was reported in the paper. It shows the process from design through manufacturing with integrated analysis.

This paper aims to use a data-driven approach towards optimizing construction operations. To this extent, it presents a machine learning (ML)-aided optimization approach, wherein the construction cost is predicted as a function of time, resources and environmental impact, which is further used as a surrogate model for cost optimization.

Taking a dataset from literature, the paper has applied various ML algorithms, namely, simple and regularized linear regression, random forest, gradient boosted trees, neural network and Gaussian process regression (GPR) to predict the construction cost as a function of time, resources and environmental impact. Further, the trained models were used to optimize the construction cost applying single-objective (with and without constraints) and multi-objective optimizations, employing Bayesian optimization, particle swarm optimization (PSO) and non-dominated sorted genetic algorithm.

The results presented in the paper demonstrate that the ensemble methods, such as gradient boosted trees, exhibit the best performance for construction cost prediction. Further, it shows that multi-objective optimization can be used to develop a Pareto front for two competing variables, such as cost and environmental impact, which directly allows a practitioner to make a rational decision.

Note that the sequential nature of events which dictates the scheduling is not considered in the present work. This aspect could be incorporated in the future to develop a robust scheme that can optimize the scheduling dynamically.

The paper demonstrates that a ML approach coupled with optimization could enable the development of an efficient and economic strategy to plan the construction operations.

The assembly of large component in out-field is an important part for the usage and maintenance of aircrafts, which is mostly manually accomplished at present, as the commonly used large-volume measurement systems are usually inapplicable. This paper aims to propose a novel coaxial alignment method for large aircraft component assembly using distributed monocular vision.

For each of the mating holes on the components, a monocular vision module is applied to measure the poses of holes, which together shape a distributed monocular vision system. A new unconstrained hole pose optimization model is developed considering the complicated wearing on hole edges, and it is solved by a iterative reweighted particle swarm optimization (IR-PSO) method. Based on the obtained poses of holes, a Plücker line coordinates-based method is proposed for the relative posture evaluation between the components, and the analytical solution of posture parameters is derived. The required movements for coaxial alignment are finally calculated using the kinematics model of parallel mechanism.

The IR-PSO method derived more accurate hole pose arguments than the state-of-the-art method under complicated wearing situation of holes, and is much more efficient due to the elimination of constraints. The accuracy of the Plücker line coordinates-based relative posture evaluation (PRPE) method is competitive with the singular value decomposition (SVD) method, but it does not rely on the corresponding of point set; thus, it is more appropriate for coaxial alignment.

An automatic coaxial alignment system (ACAS) has been developed for the assembly of a large pilotless aircraft, and a coaxial error of 0.04 mm is realized.

The IR-PSO method can be applied for pose optimization of other cylindrical object, and the analytical solution of Plücker line coordinates-based axes registration is derived for the first time.