Performance comparison of Genetic Algorithm, Differential Evolution and Particle Swarm Optimization towards benchmark functions

Lim, Seng Poh; Haron, Habibollah

doi:10.1109/icos.2013.6735045

Cited by 37 publications

(12 citation statements)

References 20 publications

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“…Equations (34)- (37) represent the control signals necessary for path tracking. On the other hand, Equations (38) and (39) are used to determine the warping and pitch angles necessary for tracking the reference. The only value that remains unknown is α, so tuning of the controller is required.…”

Section: Backstepping Controlmentioning

confidence: 99%

“…UAV systems have been studied as a whole or in subsystems (for example, tilt control). The challenge of these vehicles is that they are nonlinear, subacted, and multivariable systems, subject to unpredictable disturbances such as the interaction of the rotor flow with the quadrotor chassis or atmospheric turbulence [26][27][28][29][30][31][32][33][34][35][36][37][38][39], so that classic control (linear and invariant over time) has limited results where instabilities can occur when the system moves away from equilibrium.…”

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithm-Based Tuning of Backstepping Controller for a Quadrotor-Type Unmanned Aerial Vehicle

et al. 2020

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Backstepping is a control technique based on Lyapunov’s theory that has been successfully implemented in the control of motors and robots by several nonlinear methods. However, there are no standardized methods for tuning control gains (unlike the PIDs). This paper shows the tuning gains of the backstepping controller, using Genetic Algorithms (GA), for an Unmanned Aerial Vehicle (UAV), quadrotor type, designed for autonomous trajectory tracking. First, a dynamic model of the vehicle is obtained through the Newton‒Euler methodology. Then, the control law is obtained, and self-tuning is performed, through which we can obtain suitable values of the gains in order to achieve the design requirements. In this work, the establishment time and maximum impulse are considered as such. The tuning and simulations of the system response were performed using the MATLAB-Simulink environment, obtaining as a result the compliance of the design parameters and the correct tracking of different trajectories. The results show that self-tuning by means of genetic algorithms satisfactorily adjusts for the gains of a backstepping controller applied to a quadrotor and allows for the implementation of a control system that responds appropriately to errors of different magnitude.

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Section: Backstepping Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Based Tuning of Backstepping Controller for a Quadrotor-Type Unmanned Aerial Vehicle

et al. 2020

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“…These optimization algorithms have different characteristics, but GA has been proven to have a better performance in many cases. Goyal et al [25] did a performance comparative analysis by using ACO, GA and PSO respectively in solving the Travelling salesman problem, which showed that GA is a better approach than other algorithms Lim et al [26] compared the performance of GA, DE and PSO by using the same parameters setting for same optimization problems, where GA was proven to have a better performance than DE and PSO. Furthermore, GA has also been widely used in solving task scheduling problems in distributed systems.…”

Section: Related Workmentioning

confidence: 99%

A Genetic Algorithm Based Data Replica Placement Strategy for Scientific Applications in Clouds

Cui

Zhang

Yue

et al. 2018

IEEE Trans. Serv. Comput.

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Cloud computing is a promising distributed computing platform for big data applications, e.g., scientific applications, since excessive resources can be obtained from cloud services for processing and storing both existing and generated application datasets. However, when tasks process big data stored in distributed data centers, the inevitable data movements will cause huge bandwidth cost and execution delay. In this paper, we construct a tripartite graph based model to formulate the data replica placement problem and propose a genetic algorithm based data replica placement strategy for scientific applications to reduce data transmissions in cloud. Our approach can reduce 1) the size of moved data, 2) the time of data movement and 3) the number of movements. We conduct experiments to compare the proposed strategy with the random placement strategy used in Hadoop Distributed Files System (HDFS), which demonstrates that our strategy has better performance for scientific applications in clouds.

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“…Behaviors present in bee colonies are also taken into account to solve problems of research and exploration [12] Other optimization algorithms with this focus have been inspired by swarms of animals such as birds and fish, spawning optimization based on particle swarm. Meanwhile, genetic algorithms and differential evolution attempt to emulate nature's process in improving a species over time [13].…”

Section: Bio-inspired Optimizationmentioning

confidence: 99%

Controller optimization for a solar tracking system using differential evolution

Cuchungo

Vargas

2015

TECCIENCIA

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This paper details the design and implementation of two analog controllers for the solar tracker located in the thermal power plant laboratory at the Universidad Nacional de Colombia. In this study we used differential evolution for the optimization of the controllers. In addition to the design of the controllers, it is possible with this approach to observe the characteristics of this bio-inspired algorithm. As a result, we first present the optimization process and subsequently the dynamic response of the controllers implemented.Keywords: differential evolution, control, optimization, solar power. ResumenEste artículo detalla el diseño y la implementación de dos controladores análogos para un rastreador solar localizado en el laboratorio de plantas térmicas en la Universidad Nacional de Colombia. En este estudio se utilizó la evolución diferencial para la optimización de los controladores. Además del diseño de los controladores es posible con este enfoque observar las características de este algoritmo bioinspirado. Como resultado primero presentamos la optimización del proceso y posteriormente la respuesta dinámica del controlador implementado.

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Performance comparison of Genetic Algorithm, Differential Evolution and Particle Swarm Optimization towards benchmark functions

Cited by 37 publications

References 20 publications

Genetic Algorithm-Based Tuning of Backstepping Controller for a Quadrotor-Type Unmanned Aerial Vehicle

Genetic Algorithm-Based Tuning of Backstepping Controller for a Quadrotor-Type Unmanned Aerial Vehicle

A Genetic Algorithm Based Data Replica Placement Strategy for Scientific Applications in Clouds

Controller optimization for a solar tracking system using differential evolution

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