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Hello! I'm Daniel, a systems engineer dedicated to software development with a passion for technology and innovation. With expertise in DevOps, software architecture, and a deep interest in artificial intelligence, I strive to create advanced technological solutions that drive change and continuous improvement.

As an advocate for scientific knowledge and research, I've contributed to various academic projects and published articles in conferences and scientific journals. Here are some of my notable works:

• ANT COLONY-BASED METAHEURISTIC IN N STAGES FOR HIGH-DIMENSIONAL PROBLEMS

Ant Colony Optimization is a population metaheuristic inspired by the behavior of natural ants, specifically their ability to find the shortest path between their nest and the food source. This search mechanism has been tested in discrete problems, establishing itself as a good option for this field of application. In previous works, it was shown that dividing the exploration process of these algorithms into 2 stages considerably improves their performance in terms of time and the quality of the results. In this context, we present, in this work, a generalization of the exploitation process by stages for instances of the Medium and High-Dimension of the Traveling Salesman Problem. For the tests, 5 instances of different sizes were selected and 4 variants of the algorithm were analyzed. The results corroborated that the process of division into stages is good for the performance of the algorithm, reaching the best results with 4 stages.

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• Study of Role Replacement Strategies in Grey Wolf Optimization

Improving the behavior of metaheuristic algorithms has been, is and will be a challenge for the scientific community. Strategies aimed at improving exploration of the search space and avoiding stagnation of solutions are some of the most studied premises in the literature. The Gray wolf-based optimization (GWO) metaheuristic is capable of solving continuous optimization problems by applying a command role assignment scheme that provides an adequate balance between exploration and intensification of solutions. In this article, we will analyze some strategies for defining roles in GWO and measure their influence on the quality of the search process in a continuous space. For strategies, a probabilistic selection method is used, by distance followers and a combination of both. The experimental results showed that the follower-based variants provide greater stability in the results, and in addition, the probability-based models present greater effectiveness under a probability value of 0.9.

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