Cubature kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems

This study introduces a new single-agent metaheuristic algorithm, named cubature Kalman optimizer (CKO). The CKO is inspired by the estimation ability of the cubature Kalman filter (CKF). In control system, the CKF algorithm is used to estimate the true value of a hidden quantity from an observation signal that contain an uncertainty. As an optimizer, the CKO agent works as individual CKF to estimate an optimal or a near-optimal solution. The agent performs four main tasks: solution prediction, measurement prediction, and solution update phases, which are adopted from the CKF. The proposed CKO is validated on CEC 2014 test suite on 30 benchmark functions. To further validate the performance, the proposed CKO is compared with well-known algorithms, including single-agent finite impulse response optimizer (SAFIRO), single-solution simulated Kalman filter (ssSKF), simulated Kalman filter (SKF), asynchronous simulated Kalman filter (ASKF), particle swarm optimization algorithm (PSO), genetic algorithm (GA), grey wolf optimization algorithm (GWO), and black hole algorithm (BH). Friedman's test for multiple algorithm comparison with 5% of significant level shows that the CKO offers better performance than the benchmark algorithms

Multi-Agent cubature Kalman optimizer : A novel metaheuristic algorithm for solving numerical optimization problems

Optimization problems arise in diverse fields such as engineering, economics, and industry. Metaheuristic algorithms, including the Simulated Kalman Filter (SKF), have been developed to solve these problems. SKF, inspired by the Kalman Filter (KF) in control engineering, requires three parameters (initial error covariance P(0), measurement noise Q, and process noise R). However, studies have yet to focus on tuning these parameters. Furthermore, no significant improvement is shown by the parameter-less SKF (with randomized P(0), Q, and R). Randomly choosing values between 0 and 1 may lead to too small values. As an estimator, KF raises concerns with excessively small Q and R values, which can introduce numerical stability issues and result in unreliable outcomes. Tuning parameters for SKF is a challenging and time-consuming task. The Multi-Agent Cubature Kalman Filter (MACKO), inspired by the Cubature Kalman filter (CKF), was introduced in this work. The nature of the Cubature Kalman filter (CKF) allows the use of small values for parameters P(0), Q, and R. In the MACKO algorithm, Cubature Transformation Techniques (CTT) are employed. CTT can use small values for parameters P(0), Q, and R, so CKF was developed to overcome KF and other estimation algorithms. Moreover, in CTT, the term local neighborhoods is used to propagate the cubature point in local search, where the radius, δ, of local search is updated in every iteration to balance between the exploration and exploitation processes. MACKO is evaluated on the CEC 2014 benchmark suite with 30 optimization problems, and its performance is compared with nine existing metaheuristic algorithms. Simulation results demonstrate that MACKO is superior, outperforming the benchmark algorithms, as indicated by Friedman's test with a 5 % significance level

Planeacion de trayectorias en manipuladores seriales soldadores basada en optimizacion de energia electrica y manipulabilidad

En el presente trabajo se desarrolla una metodología de planeación de trayectorias para manipuladores seriales antropomórficos de seis grados de libertad y muñeca esférica enfocada en la minimización del consumo eléctrico y maximización de la manipulabilidad. El trasfondo (undercurrent) de esta metodología, es garantizar un aumento en la productividad maximizando el rendimiento del manipulador. Para lograr tal fin se propone un espacio de trabajo esférico simplificado, se plantean los modelos de cálculo de la cinemática directa, inversa y diferencial, al igual que la dinámica inversa de los manipuladores, y se integran en un algoritmo de optimización el cual tiene como base el algoritmo heurístico de Kalman. El algoritmo de optimización se evalúa mediante el análisis del comportamiento de cinco trayectorias realizadas en los manipuladores PUMA 560 y KUKA KR5 HW ARC, simuladas en un programa desarrollado en la aplicación de Visual Basic perteneciente al software Autodesk Inventor. El enfoque de planeación de trayectoria propuesto permite ser usado en manipuladores antropomórficos de seis grados de libertad y muñeca esférica, de los cuales se conozca sus parámetros cinemáticos y dinámicos, para generar trayectorias optimizadas desde los criterios de manipulabilidad y energía eléctrica, conservando la orientación del efector final. Una ventaja importante que posee el método es que a partir de una trayectoria planteada en forma de coordenadas cartesianas XYZ y orientaciones del efector en ángulos tipo Euler (precesión, nutación y rotación propia), buscará una solución óptima dentro del espacio de trabajo simplificado del manipulador respetando sus limites articulares y de velocidad, permitiendo con esto tener trayectorias de entrada en las cuales solo importe la orientación de la herramienta a lo largo del recorrido, situación indispensable para el proceso de soldadura.Magister en Automatización y Contro