Data-driven fractional-order PID controller tuning for liquid slosh suppression using marine predators algorithm

Traditional control system development for liquid slosh problems often relies on model-based approaches, which are challenging to implement in practice due to the chaotic and complex nature of fluid motion in containers. In response, this study introduces a data-driven fractional-order PID (FOPID) controller designed using the Marine Predators Algorithm (MPA) for suppressing liquid slosh. The MPA serves as a data-driven tuning tool to optimize the FOPID controller parameters based on a fitness function comprising the total norms of tracking error, slosh angle, and control input. A motor-driven liquid container undergoing horizontal motion is employed as a mathematical model to validate the proposed data-driven control methodology. The effectiveness of the MPA-based FOPID controller tuning approach is assessed through the convergence curve of the average fitness function, statistical results, Wilcoxon's rank test, and the ability to track the cart's horizontal position while minimizing the slosh angle and control input energy. The proposed data-driven tuning tool demonstrates superior performance compared to other recent metaheuristic optimization algorithms across the majority of evaluation criteria

Sensitivity of shortest distance search in the ant colony algorithm with varying normalized distance formulas

The ant colony algorithm is an algorithm adopted from the behavior of ants which naturally ants are able to find the shortest route on the way from the nest to places of food sources based on footprints on the track that has been passed. The ant colony algorithm helps a lot in solving several problems such as scheduling, traveling salesman problems (TSP) and vehicle routing problems (VRP). In addition, ant colony has been developed and has several variants. However, in its function to find the shortest distance is optimized by utilizing several normalized distance formulas with the data used in finding distances between merchants in the mercant ecosystem. Where in the test normalized distance is superior Hamming distance in finding the shortest distance of 0.2875, then followed by the same value, namely the normalized formula Manhattan distance and normalized Euclidean distance with a value of 0.4675 and without using the normalized distance formula or the original ant colony algorithm gets a value 0.6635. Given the sensitivity in distance search using merchant ecosystem data, the method works well on the ant colony Algorithm using normalized Hamming distance

Optimal integration of D-STATCOM in distribution grids for annual operating costs reduction via the discrete version sine-cosine algorithm

This paper deals with the problem of the optimal placement and sizing of distribution static compensators (D-STATCOM) in electrical distribution networks to reduce the total annual operative costs associated with the total costs of energy losses added with the investment costs in D-STATCOM. The metaheuristic sine-cosine optimization algorithm determines nodes with the location and optimal sizes of the D-STATCOM. A discrete-continuous codification represents the decision variables, where the discrete part is entrusted with the best candidate nodes selection. The continuous part deals with the optimal sizes assigned to the D-STATCOM. Numerical results in the IEEE 33- and IEEE 69-bus systems demonstrate the effectiveness of this approach since it helps to minimize the total grid operation costs compared with the solution of the mixed-integer nonlinear programming model in GAMS. All the numerical validations are carried out in the MATLAB programming environment. © 2022 The Author

Parameter estimation of a thermoelectric generator by using salps search algorithm

Thermoelectric generators (TEGs) have the potential to convert waste heat into electrical energy, making them attractive for energy harvesting applications. However, accurately estimating TEG parameters from industrial systems is a complex problem due to the mathematical complex non-linearities and numerous variables involved in the TEG modeling. This paper addresses this research gap by presenting a comparative evaluation of three optimization methods, Particle Swarm Optimization (PSO), Salps Search Algorithm (SSA), and Vortex Search Algorithm (VSA), for TEG parameter estimation. The proposed integrated approach is significant as it overcomes the limitations of existing methods and provides a more accurate and rapid estimation of TEG parameters. The performance of each optimization method is evaluated in terms of root mean square error (RMSE), standard deviation, and processing time. The results indicate that all three methods perform similarly, with average RMSE errors ranging from 0.0019 W to 0.0021 W, and minimum RMSE errors ranging from 0.0017 W to 0.0018 W. However, PSO has a higher standard deviation of the RMSE errors compared to the other two methods. In addition, we present the optimized parameters achieved through the proposed optimization methods, which serve as a reference for future research and enable the comparison of various optimization strategies. The disparities observed in the optimized outcomes underscore the intricacy of the issue and underscore the importance of the integrated approach suggested for precise TEG parameter estimation

Microalgal cultures for the remediation of wastewaters with different nitrogen to phosphorus ratios: Process modelling using artificial neural networks

Microalgae have remarkable potential for wastewater bioremediation since they can efficiently uptake nitrogen and phosphorus in a sustainable and environmentally friendly treatment system. However, wastewater composition greatly depends on its source and has a significant seasonal variability. This study aimed to evaluate the impact of different N:P molar ratios on the growth of Chlorella vulgaris and nutrient removal from synthetic wastewater. Furthermore, artificial neural network (ANN) threshold models, optimised by genetic algorithms (GAs), were used to model biomass productivity (BP) and nitrogen/phosphorus removal rates (RRN/RRP). The impact of various inputs culture variables on these parameters was evaluated. Microalgal growth was not nutrient limited since the average biomass productivities and specific growth rates were similar between the experiments. Nutrient removal efficiencies/rates reached 92.0 +/- 0.6%/6.15 +/- 0.01 mgN L-1 d-1 for nitrogen and 98.2 +/- 0.2%/0.92 +/- 0.03 mgP L-1 d-1 for phosphorus. Low nitrogen concentration limited phosphorus uptake for low N:P ratios (e.g., 2 and 3, yielding 36 +/- 2 mgDW mgP-1 and 39 +/- 3 mgDW mgP-1, respectively), while low phosphorus concentration limited nitrogen uptake with high ratios (e.g., 66 and 67, yielding 9.0 +/- 0.4 mgDW mgN-1 and 8.8 +/- 0.3 mgDW mgN-1, respectively). ANN models showed a high fitting performance, with coefficients of determination of 0.951, 0.800, and 0.793 for BP, RRN, and RRP, respectively. In summary, this study demonstrated that microalgae could successfully grow and adapt to N:P molar ratios between 2 and 67, but the nutrient uptake was impacted by these variations, especially for the lowest and highest N:P molar ratios. Furthermore, GA-ANN models demonstrated to be relevant tools for microalgal growth modelling and control. Their high fitting performance in characterising this biological system can contribute to reducing the experi-mental effort for culture monitoring (human resources and consumables), thus decreasing the costs of microalgae production

Feature selection using enhanced particle swarm optimisation for classification models.

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets

Design and Optimization of Optical Devices Using Artificial Intelligence Techniques

Over the last decade, there has been a growing interest in utilizing novel photonic and optical devices for a diverse range of applications. For the next generation of wireless communication networks, the development of new and optimal optical devices is inevitable. Existing optical network infrastructure cannot meet the stringent requirements of next-generation data networks (such as a 1000-fold increase in bandwidth demand, very low latency, better spectral and energy efficiency, etc.). In other words, the physical layer of the communication network must be revolutionized to provide the proper foundation for these emerging technologies. Optical networks are based on propagating light. Light propagation in realistic settings is usually a complicated phenomenon. When it comes to the context of optical devices and its propagation in the new devices, the complexity of the problem becomes much higher. In other words, the relations between the light propagation characteristics and the structural parameters of the new devices are mostly unknown. Therefore, the conventional method for designing such devices in the absence of a clear analytic description is usually based on a trial and error process. This method has many disadvantages, being time-consuming, inefficient, and the designed device is usually far from an optimized one. Also, the designing process needs intensive human involvement. Therefore, to fill this gap, we have utilized artificial intelligence (AI) techniques to design, analyze, and optimize several different optical devices. More specifically, we have proposed several optimization frameworks for designing orbital angular momentum (OAM) fibers, large mode area photonic crystal (PhC) fibers, waveguide-based LP01 to LP0m mode converter, PhC filters, PhC sensors, and PhC-enhanced light-emitting diodes (LEDs). In all of these devices, we are dealing with a complicated system in which the relationships between the structural parameters and the output performance merit factors are very complicated. Such problems have a long simulation runtime, so it is not viable to employ an exhaustive optimization algorithm, which evaluates all of the possible combinations of the parameters to find the optimal one. Therefore, we consider our problem as a black box and use the AI optimization algorithm to find the optimal solution. Eventually, the proposed optimization frameworks open up an effective way to design high-performance optical devices for a diverse range of applications and pave the way for the development of next-generation optical devices for next-generation optical networks

Reporte técnico del estado del arte sobre algoritmos evolutivos basados en descomposición para problemas con muchos objetivos: desde los algoritmos genéticos a la optimización con muchos objetivos

Este trabajo tiene como objetivo presentar el estado del arte de algoritmos evolutivos basados en descomposición para problemas con muchos objetivos. En este contexto, se presenta en un único material el desarrollo de los Algoritmos Evolutivos Multiobjetivo (Multi-objective Evolutionary Algorithms- MOEA) desde los primeros métodos aparecidos en la década de los 90s hasta los algoritmos para lidiar con problemas con muchos objetivos de optimización (many-objective optimization problems (MaOP)) de la actualidad considerando especialmente a los métodos basa dos en descomposición. Además de servir como una actualización de referencia el trabajo busca reflejar algunas áreas de investigación futura en el área y que pueden considerarse de relevancia.CONACYT - Consejo Nacional de Ciencias y TecnologíaPROCIENCI