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The Role of Optimization
Published in Louis Theodore, R. Ryan Dupont, Water Resource Management Issues, 2019
Louis Theodore, R. Ryan Dupont
With the promise of reducing design time and cost while improving product quality, design optimization held tremendous potential. Starting with a suboptimal design, a numerical optimization algorithm could be used to iteratively adjust a set of preselected design parameters to achieve a set of design targets. This new class of optimization technology enabled broader, more comprehensive, faster searches for innovative designs than was possible using previous generations of tools. Moreover, it required no expertise in optimization theory, so it was easier to use for nonexperts and experts alike. By leveraging an engineer’s potential to discover new design concepts, this new class of optimization technology overcomes the limits of human intuition and extends the designer’s professional capability to achieve breakthrough designs and accelerated innovation.
Geometric, Linear, and Dynamic Programming and Other Methods for Optimization
Published in Yogesh Jaluria, Design and Optimization of Thermal Systems, 2019
Another area of optimization that has received a lot of attention lately is that of shape optimization. In this optimization problem, the geometry or topology of the item is a variable, rather than just its dimensions. Thus, the shape of the part may be varied to minimize cost, maximize heat transfer rate, minimize weight, and so on, while the given constraints are satisfied. Generally, an initial geometry or shape is chosen and a numerical computation, usually based on the finite element method because of its versatility, is carried out to determine the objective function. The shape is changed iteratively within the feasible domain to optimize the objective function subject to the constraints. The design variables include those that define the boundary or shape of the item under consideration. Such an iterative procedure, with changes in the shape, is possible mainly because of the availability of efficient computational schemes for analysis and fast computers. These ideas have been extended to the optimization of topology, profile, trajectory, and configuration in different types of systems and applications. Though an active area for research in the design of structures, shape optimization has not been used much in thermal systems and processes. Several other methods were outlined in Chapter 7 and some examples were given in Chapter 9. These include response surfaces and multi-objective design optimization. Genetic algorithms, artificial neural networks, and fuzzy logic are other approaches that are used in the optimization process, as discussed in Chapter 7.
Recent Trends in 5G and Machine Learning, Challenges, and Opportunities
Published in Uzzal Sharma, Parmanand Astya, Anupam Baliyan, Salah-ddine Krit, Vishal Jain, Mohammad Zubair Khan, Advancing Computational Intelligence Techniques for Security Systems Design, 2023
S. Kannadhasan, R. Nagarajan, M. Shanmuganantham
We investigated the application of deep learning in applied EM. Advances in machine-learning algorithms might aid other fields, such as engineering. Since there has been no visible progress in the radar image classification, the use of the radar image classification in the antenna design field is intended to enable non-experienced engineers by aiding in the design of conventional antennas or discovering innovative antenna designs. In order to get high numerical performance, the optimization of architectural parameters is currently the most significant problem, as shown in Figure 5.2. The literature has suggested genetic algorithms (GA), particle swarm optimization (PSO), Biogeography Based Optimization (BBO), neural networks, and other approaches for design optimization. The optimization methodology is a method for determining the minimum and maximum of a cost function-defined operator. The optimizers alter the vector equations before the lowest has a firm hold on anything. The error function (EF) or search methods (SM) formulas identify the optimizers. The majority of the above-mentioned optimization techniques may be applied in simulation applications. The electromagnetic simulation software HFSS and CST employ one or more of the mathematical optimizers listed below to obtain a broad variety of capabilities: Non-linear programming approaches include ANN (MATLAB compatible), Quasi Newton, Sequential Non-linear Programming (SNLP), Sequential Mixed Integer Non-linear Programming (SMINLP), and GA. ANN allows you to customize antenna characteristics including return loss, bandwidth, scale, and gain. To increase energy economy while preserving users' quality of service (QoS) needs, a collaborative architecture for antenna selection and power delivery for multi-user multi-antenna downlinks was developed.
Enhancing ball grid array (BGA) component design and reliability using a novel reliability-based design optimization (RBDO) methodology
Published in Mechanics of Advanced Materials and Structures, 2023
Sinda Ghenam, Abdelkhalak El Hami, Khalil Dammak, Wajih Gafsi, Ali Akrout, Mohamed Haddar
To improve the performance and reduce the size of electronic components, several types of geometric optimization methods can be used. Among the most commonly used methods are shape optimization, location optimization, component layout optimization, size optimization, and material optimization. Shape optimization involves changing the shape of the component to improve its performance, while location optimization involves changing its position to achieve more accurate readings. Component layout optimization is achieved through arranging the components to minimize electromagnetic interference. Size optimization reduces the size of components while maintaining performance and material optimization involve the use of high-performance materials, such as those with higher thermal conductivity meant, to improve heat dissipation. These methods can be used independently or in combination in order to optimize the performance and size of electronic components. In what follows, we are basically interested in design optimization known as size optimization. Its principal target is to find the best combination of cost reduction and performance increase. Starting from an initial design that is defined by design variables, this optimization aims to iteratively generate the optimal design that meets the performance criteria. The design optimization process is referred to as the search, in the design phase, for parameters that not only minimize an objective function but also satisfy mechanical, physical, and geometrical performance constraints.
Multi-objective optimization design of energy efficiency for office building window systems based on indoor thermal comfort
Published in Science and Technology for the Built Environment, 2023
Xudong Zhang, Qiao Ning, Zengcheng Chen
Among the research papers on architectural design optimization, some popular optimization algorithms are genetic algorithm (GA), particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA), ant colony optimization algorithm and hybrid algorithm, etc. (Machairas, Tsangrassoulis, and Axarli 2014). Among these algorithms, GA is usually able to obtain better optimization results faster when solving more complex combinatorial optimization problems compared with some conventional optimization algorithms. As a variant developed on the basis of GA, NSGA-II is one of the most popular multi-objective optimization algorithms, which reduces the complexity of the non-inferior sorting genetic algorithm and has the advantages of faster running speed and better convergence of solution sets, making it a benchmark for other multi-objective optimization algorithms. NSGA-II is one of the most widely used multi-objective optimizers involving continuous, discrete and hybrid variables (Yang et al. 2017), while it is able to order the non-dominated solutions efficiently to provide Pareto optimal solutions and accelerate the convergence by considering elitism compared to other optimization algorithms (Ghaderian and Veysi 2021). NSGA-II can better meet the needs of this study and shorten simulation time by considering the characteristics of optimization variables in this study, so it is chosen as the optimization algorithm for multi-objective optimization design in this paper.
Design of nodule-lifting apparatus of seabed mining electric vehicle considering physical properties of polymetallic nodules
Published in Marine Georesources & Geotechnology, 2023
Saekyeol Kim, Su-gil Cho, Jae Wan Park, Tae Hee Lee, Jong-Su Choi, Sanghyun Park, Sup Hong, Hyung-Woo Kim, Cheon-Hong Min, Young-Tak Ko, Sang-Bum Chi
Design optimization is an engineering design method that uses the mathematical formulation of a design problem. During the optimization process, a response measure is maximized or minimized while all other constraints are satisfied through a numerical algorithm. Most industrial problems require highly sophisticated models to obtain highly accurate response predictions. However, the design optimization of such large and complex systems is extremely difficult because the evaluation of the objective and constraint functions is computationally expensive, and the gradients of these functions are sometimes difficult to derive or calculate (Arora 2016). To obtain a better design without requiring an enormous number of function calculations for design optimization, a discrete design methodology was developed. For unconstrained design optimization, this methodology calculates the objective function on the design points in a predefined design table and chooses the best feasible point, while penalty functions are employed to deal with problems with design constraints.