Thermoelectric generation with reduced pollutants made possible by bio-inspired computing

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.

Breaking of the Trade-Off Principle between Computational Universality and Efficiency by Asynchronous Updating

Although natural and bioinspired computing has developed significantly, the relationship between the computational universality and efficiency beyond the Turing machine has not been studied in detail. Here, we investigate how asynchronous updating can contribute to the universal and efficient computation in cellular automata (CA). First, we define the computational universality and efficiency in CA and show that there is a trade-off relation between the universality and efficiency in CA implemented in synchronous updating. Second, we introduce asynchronous updating in CA and show that asynchronous updating can break the trade-off found in synchronous updating. Our finding spells out the significance of asynchronous updating or the timing of computation in robust and efficient computation.

The computational power of timed P systems with active membranes using promoters

P systems with active membranes are a class of bioinspired computing models, where the rules are used in the non-deterministic maximally parallel manner. In this paper, first, a new variant of timed P systems with active membranes is proposed, where the application of rules can be regulated by promoters with only two polarizations. Next, we prove that any Turing computable set of numbers can be generated by such a P system in the time-free way. Moreover, we construct a uniform solution to the$\mathcal{SAT}$problem in the framework of such recognizer timed P systems in polynomial time, and the feasibility and effectiveness of the proposed system is demonstrated by an instance. Compared with the existing methods, the P systems constructed in our work require fewer necessary resources and RS-steps, which show that the solution is effective toNP-complete problem.

Joint Angle-Amplitude Estimation for Multiple Signals with L-Structured Arrays Using Bioinspired Computing

The aim of this work is to estimate jointly the elevation and azimuth angles along with the amplitudes of multiple signals impinging on 1-L- and 2-L-shape arrays. An efficient mechanism based on hybrid Bioinspired techniques is proposed for this purpose. The global search optimizers such as Differential Evolution (DE) and Particle Swarm optimization (PSO) are hybridized with a local search optimizer called pattern search (PS). Approximation theory in Mean Square Error sense is exploited to develop a fitness function of the problem. The unknown parameters of multiple signals transmitted by far-field sources are estimated with the strength of hybrid DE-PS and PSO-PS. The effectiveness of the proposed techniques is tested in terms of estimation accuracy, proximity effect, convergence, and computational complexity.