In this work, the Finite Element Method is used for finding the numerical solution of an elliptic problem with Henstock–Kurzweil integrable functions. In particular, Henstock–Kurzweil high oscillatory functions were considered. The weak formulation of the problem leads to integrals that are calculated using some special quadratures. Definitions and theorems were used to guarantee the existence of the integrals that appear in the weak formulation. This allowed us to apply the above formulation for the type of slope bounded variation functions. Numerical examples were developed to illustrate the ideas presented in this article.
Leveraging human insight and intuition has been identified as having the potential for the improvement of traditional algorithmic methods. For example, in a video game, a user may not only be entertained but may also be challenged to beat the score of another player; additionally, the user can learn complicated concepts, such as multi-objective optimization, with two or more conflicting objectives. Traditional methods, including Tabu search and genetic algorithms, require substantial computational time and resources to find solutions to multi-objective optimization problems (MOPs). In this paper, we report on the use of video games as a way to gather novel solutions to optimization problems. We hypothesize that humans may find solutions that complement those found mechanically either because the computer algorithm did not find a solution or because the solution provided by the crowdsourcing of video games approach is better. We model two different video games (one for the facility location problem and one for scheduling problems), we demonstrate that the solution space obtained by a computer algorithm can be extended or improved by crowdsourcing novel solutions found by humans playing a video game.
The purpose of this article is to study numerically the Turing diffusion-driven instability mechanism for pattern formation on curved surfaces embedded in
ℝ
3
, specifically the surface of the sphere and the torus with some well-known kinetics. To do this, we use Euler’s backward scheme for discretizing time. For spatial discretization, we parameterize the surface of the torus in the standard way, while for the sphere, we do not use any parameterization to avoid singularities. For both surfaces, we use finite element approximations with first-order polynomials.
The facility location problem (FLP) is a complex optimization problem that has been widely researched and applied in industry. In this research, we proposed two innovative approaches to complement the limitations of traditional methods, such as heuristics, metaheuristics, and genetic algorithms. The first approach involves utilizing crowdsourcing through video game players to obtain improved solutions, filling the gap in existing research on crowdsourcing for FLP. The second approach leverages machine learning techniques, specifically prediction methods, to provide an efficient exploration of the solution space. Our findings indicate that machine learning techniques can complement existing solutions by providing a more comprehensive approach to solving FLP and filling gaps in the solution space. Furthermore, machine learning predictive models are efficient for decision making and provide quick insights into the system’s behavior. In conclusion, this research contributes to the advancement of problem-solving techniques and has potential implications for solving a wide range of complex, NP-hard problems in various domains.
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