Vulnerability analysis is an emerging technique that discovers concise descriptions of the
conditions that lead to decision-relevant outcomes (ie, scenarios) by applying machine …

A methodology that could reduce computational cost and time, combining computational
fluid dynamics (CFD) simulations, neural networks, and genetic algorithms to determine a …

In real-world scenarios where resources for evaluating expensive optimization problems are
limited and the reliability of trained models is hard to assess, the quality of the non …

The evolutionary algorithm has been extensively used to solve a range of combinatorial
optimization problems. The adaptability of evolutionary algorithm mechanisms provides …

Occupant behavior has an important impact on building energy consumption, and the
accuracy of an occupant behavior model directly affects the reliability of energy consumption …

The horticultural industry in Nordic countries is highly dependent on greenhouse systems
due to the limitation of the natural environment and the strict planting requirements of …

In many-objective optimization problems (MaOPs), balancing convergence and diversity
while rapidly converging to the Pareto front is an arduous task for evolutionary algorithms. In …

Analog circuits are increasingly used as controllers for complex fuzzy systems, prompting
the need for hardware implementation of fuzzy logic solutions and, depending on the …

The ultimate goal of solving Multimodal Multiobjective Optimization Problems (MMOPs) is to
identify all Pareto optimal solution sets that are equivalent. Strong search capability and …

The deployment of security guards in large public spaces is a promising research topic with
a wide range of applications. Existing methods are mainly based on manual design …