Aprendizaje incremental basado en población como buena alternativa al uso de algoritmos genéticos

Prospero, Luis A. Cruz; Lavalle, Manuel Mejía; Ascencio, Jose Ruiz; Rincón, Virna V. Vela

doi:10.13053/rcs-116-1-4

Cited by 3 publications

(3 citation statements)

References 14 publications

(17 reference statements)

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“…The metaheuristics are not the solution to everything and many times are often less efficient tan specific heuristics, in several orders of magnitude, in problems that accept this type of pure heuristics; however, the advantage of metaheuristics is exploited, when are applied in problems that do not have a specific algorithm or heuristic to obtain a satisfactory solution, or when it is not feasible to implement an optimal method. Metaheuristics are widely recognized as one of the best approaches to tackle combinatorial optimization problems [14], so they have become popular in the last couple of decades for its characteristics of simplicity, flexibility, by having free bypass mechanism and that can avoid falling into local optima.…”

Section: Metaheuristicsmentioning

confidence: 99%

Solution of the Assignment of Schedules Problem with the Gray Wolf Optimizer (GWO), Applying Evolutionary Parameters

Bautista¹,

Canul-Reich²,

Díaz³

et al. 2018

RCS

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The educational institutions are faced with the problem of assignment their educational resources (students, teachers, classrooms, material) each scholar period, so there is a need to look for new software tools to satisfy this goals, due to the great amount of possible restrictions that has the assignment schedule problem, it is convenient to use metaheuristics algorithms to obtain acceptable solutions in reasonable times, for this reason, the present article shows the proposal for the resolution of the assignment schedule problem in the educational institutions using the metaheuristic GWO, taking as a use case a public university, a private university and a higher education institute. The test was carried out in a private university, obtaining better results in comparison with a genetic algorithm, demonstrating the potential of the metaheuristic GWO with evolutionary parameters.

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Section: Metaheuristicsmentioning

confidence: 99%

Solution of the Assignment of Schedules Problem with the Gray Wolf Optimizer (GWO), Applying Evolutionary Parameters

Bautista¹,

Canul-Reich²,

Díaz³

et al. 2018

RCS

View full text Add to dashboard Cite

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“…Self-tuning PID controller is required, because of the variation when changing different vehicles, is here where de adaptability and evolution in a GA allows getting a tuning method for any car [20].…”

Section: Introductionmentioning

confidence: 99%

“…The selection of a GA as tuning method it is the advantage that represents at the moment to find results in an optimization problem, where the solution presents a nonlinear behavior and in a multi-objective solution, gives a robustness method [20].…”

Section: Introductionmentioning

confidence: 99%

Steering Wheel Control in Lane Departure Warning System

Rojas¹,

Arellano²,

González³

et al. 2018

RCS

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Advanced driver assistance systems (ADAS) are important to prevent road accidents, one of those are lane invasions. Nowadays optical and control systems are applied in new cars to maintain vehicles centered in a lane, nevertheless old vehicles cannot access to these new assistant systems. In this paper we present a position steering wheel control (SWC) in a lane detection prototype designed for its implementation in cars that do not have installed this system. The proposed technique uses a self-tuning PID controller connected to a DC motor for SWC that uses orientation of lines on the road. Lines are detected by applying Sobel filter convolution and Hough transform, which are image processing techniques that allow obtaining orientation. The PID controller is automatically tuned since this assistant would be installed in different cars. GA is used since it has shown good results parametrizing well known structured problems without requiring mathematical models or techniques that demand control experts. The main contributions of this research are a SCW alternative capable of been installed in any old vehicle and the algorithm implemented for using orientation as input data as the control reference with a self-tuning PID controller.

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Dynamic Demand Modeling Incorporating Renewable Energy Sources Using a Population-Based Optimization Method

Guzmán,

Ospina,

Martínez

et al. 2024

Wseas Transactions on Power Systems

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Due to the inclusion of distributed generation (DG) in microgrids (MGs), the accelerated growth in demand, and environmental concerns, suitable management and operational strategies are imperative. The utilization of wind and solar energy has rapidly increased in MGs. However, due to the uncertainties these systems present, accurately predicting energy generation remains challenging. This necessitates modeling the system’s random variables (such as renewable resource output and possibly load demand) using appropriate and feasible methods. The primary objective of this article is to determine the optimal setpoints for renewable energy sources (RES) and all elements involved in the MG, minimizing the total operation cost. The system comprises wind turbines (WT), photovoltaic panels (PV), energy storage systems (ESS), and electric vehicles (EVs). Weibull distribution and the Hottel and Liu Jordan equations are employed to determine the potential available capacity of wind and solar energy generation, respectively. ESS is utilized to enhance MG performance. For optimal management, a comprehensive mathematical model with practical constraints for each MG element is extracted. An efficient Population-Based Incremental Learning (PBIL) metaheuristic method is proposed to solve the optimization objective in an MG, demonstrating that this energy management system optimizes and effectively coordinates DG and ESS energy generation considering economic considerations. Finally, PBIL is compared with a commonly used model, Particle Swarm Optimization (PSO), across various scenarios, analyzing and evaluating their outcomes, showcasing a reduction in operation costs.

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Aprendizaje incremental basado en población como buena alternativa al uso de algoritmos genéticos

Cited by 3 publications

References 14 publications

Solution of the Assignment of Schedules Problem with the Gray Wolf Optimizer (GWO), Applying Evolutionary Parameters

Solution of the Assignment of Schedules Problem with the Gray Wolf Optimizer (GWO), Applying Evolutionary Parameters

Steering Wheel Control in Lane Departure Warning System

Dynamic Demand Modeling Incorporating Renewable Energy Sources Using a Population-Based Optimization Method

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