The debate to establish a balance between the generation of electricity and the preservation of the environment is, extraordinarily, important. This article proposes, as a short-term solution, the replacement of diesel oil by natural gas in thermoelectric generation. Natural gas emits 75% less pollutants to the environment than diesel and has a similar energetic efficiency. As a strategy for this replacement to occur safely, the computational modeling was developed in a Bioinspired Computing methodology, called Genetic Algorithm (GA). The GA incorporated all the variables of the electricity and natural gas networks, presented in the mathematical modeling. The result was a significant reduction in the level of pollutants emitted, with high stability in the electrical power system.

This article portrays the relationship between fractional order differential calculus and the computational intelligence method, applying it to the improvement of intelligent systems. The Kirchhoff Laws, represented by second order differential equations, were solved via non-integer order differential calculus. The results obtained were used in the implementation of decision trees, which allowed the decision rules to be incorporated into the controllers. The results obtained by mathematical modeling did magnify the information extracted from Kirchhoff's Laws. Due to the gain magnitude of this information, the decision trees were obtained with greater precision and accuracy. In this way, it was achieved to build a hybrid system capable of being used in the development of controllers automata that has the lower response time and highest efficiency.

The numerous scientific works, recently released, on projects applied to sustainable development, have considered a combination of methods such as AHP (Analytical Hierarchy Process) and GIS (Geographic Information Systems), for example. These procedures enable the identification of the main criteria, useful in the cadastral formulation, in a numerical format, for the problem of solid waste. This article presents a Mathematical and Computational Modeling Process (PM₂C), which benefits from the response managed by the AHP and GIS methods, implementing functional relationships that adjust the n dimensions of the Criteria Matrix. The main objective of this work is to establish the Conventional and Optimal Solutions for the problem of the area location conducive to the construction of a landfill. The strategies, mathematical and computational, employ the Cramer Method and the Interior Points Method (Prime-dual) in the making of the algorithms. The results, expressed in analytical and graphical forms, show the individual values for each criterion involved in the phenomenon. The comparison, between the solution matrices, denotes the importance of incorporating the initial conditions, related to social and economic effects, and environmental security restrictions, in the modeled algorithms. Based on these premises, it was possible to simulate scenarios that demonstrate, with the highest precision, the best values of the criteria useful for decision making in the selection of the optimal area for the implementation of a landfill.

This paper aims to present and run a composite model using Genetic Algorithm (GA) and Particle Swarm (PSO), with the assistance of parallel computing methods, to optimize the electrical distribution in a power grid based on an IEEE 14-bus system. The mathematical-computational modeling allows using the objective function to analyze the cost in relation to power or voltage as independent variables, and it is the bridge for the connection between the 2 implemented algorithms. The results presented in this article demonstrate that the methodology was implemented splendidly, in addition to obtaining an excellent computational cost and complying with the physical restrictions of network security, it also achieved global solutions in its optimization.

Composite materials stand out for enabling the development of new materials that are environmentally friendly, whose manufacturing processes and waste are increasingly less harmful, associated with good mechanical properties. In this context, this work sought to investigate the mechanical behavior in flexural and impact of polyester matrix composites with the inclusion of short fibers of sisal, piassava, raffia, mallow and jute arranged randomly. The mechanical properties of flexural were evaluated according to ASTM D 790 and the impact toughness Charpy with the ASTM D 6110 standard. The results showed that when subjected to flexural stresses, the composites presented resistance of 112 .12 MPa for the neat matrix and 35.21; 34.45; 27.29; 25.89 and 20.56 MPa with the inclusion of sisal, piassava, raffia, mallow and jute fibers, respectively. As for the impact test, they showed values of 31.50 kJ/m² for the neat matrix and 57.06; 52.52; 48.03; 38.02; 37.65 kJ/m² with the inclusion of sisal, piassava, raffia, mallow and jute fibers, respectively. Then, the fracture surface of the composites was analyzed with a microscope in order to correlate the fracture aspects with the mechanical properties.

As optimization processes have become more complex for embracing problems of different characteristics, it is necessary to have multiple adequate algorithms to tackle each of such problems. Bio-inspired evolutionary algorithms are suitable solutions for these situations, but they require re-tuning when working with different systems. Autotuning is a popular strategy to increase the adaptability of optimization algorithms. Adaptive Radiation (AR) is a phenomenon in nature that optimizes a population by diversity increase and niche specialization through intense mutation. This research aimed to insert this effect into the Genetic Algorithm (GA) workflow as a biological-inspired autotuning method, creating a new model called Genetic Algorithm with Adaptive Radiation (GAAR). The implementation of the AR component resulted in consistent and improved results on multiple benchmark functions from the CEC2019 challenge. The GAAR only changes the value of the AR component, which is enough to make this model achieve the best results in 57% of the tests and the worst results in 0% of the tests, while the Adaptive Particle Swarm Optimization (APSO) presented 39% and 12% of the best and worst results, respectively.