Contemporary engineering design is heavily based on computer simulations. Accurate, high-fidelity simulations are used not only for design verification but, even more importantly, to adjust parameters of the system to have it meet given performance requi
Conversely, optimization methods can refine machine learning models for better accuracy and generalization, leading to breakthroughs in domains like healthcare, finance, and engineering. We invite contributions that highlight innovative approaches and significant findings in the intersection of machine ...
The survey compiles the majority of the current meta-heuristic models in engineering applications. The list of bio-inspired optimization strategies used to solve challenging real-time engineering challenges is fully explored in this section. It also covers the advantages and disadvantages of the most ...
As a result, the search efficiency of global search optimization algorithm in high-dimensional design space becomes low [34], [35], [36], and its values of application in engineering problems are greatly reduced accordingly [37]. Although the global surrogate models can readily be used with ...
Optimization models are designed to help organizations and individuals make informed decisions by maximizing or minimizing an objective function while adhering to specific constraints. Objective functions are the mathematical expressions that define what you want to maximize (e.g., profit, revenue, efficie...
Engineering Optimization is an interdisciplinary engineering journal which serves the large technical community concerned with quantitative computational methods of optimization, and their application to engineering planning, design, manufacture and operational processes. The policy of the journal treats ...
A new bio-inspired algorithm, Aphids Optimization Algorithm (AOA), is proposed in this paper. This algorithm simulates the foraging process of aphids with wings, including the generation of winged aphids, flight mood, and attack mood. Concurrently, the corresponding optimization models are presented...
Linear optimization, or linear programming, involves the solution of mathematical models where all of the relationships in the model are linear. Integer programming refers to problems where all of the variables are restricted to be integers. Mixed integer linear programming deals with problems where ...
Prompt-MolOpt is a tool for molecular optimization; it makes use of prompt-based embeddings, as used in large language models, to improve the transformer’s ability to optimize molecules for specific property adjustments. Notably, Prompt-MolOpt excels in working with limited multiproperty data (...
The company chose to implement an advanced spending intelligence tool. The system used data engineering and machine-learning methods to address the problem of dispersed data in the IT system. These tools extracted data from each business unit’s enterprise-resource-planning (ERP) system a...