Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques.

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area

Evolutionary strategies in swarm robotics controllers

Nowadays, Unmanned Vehicles (UV) are widespread around the world. Most of these vehicles require a great level of human control, and mission success is reliant on this dependency. Therefore, it is important to use machine learning techniques that will train the robotic controllers to automate the control, making the process more efficient. Evolutionary strategies may be the key to having robust and adaptive learning in robotic systems. Many studies involving UV systems and evolutionary strategies have been conducted in the last years, however, there are still research gaps that need to be addressed, such as the reality gap. The reality gap occurs when controllers trained in simulated environments fail to be transferred to real robots. This work proposes an approach for solving robotic tasks using realistic simulation and using evolutionary strategies to train controllers. The chosen setup is easily scalable for multirobot systems or swarm robots. In this thesis, the simulation architecture and setup are presented, including the drone simulation model and software. The drone model chosen for the simulations is available in the real world and widely used, such as the software and flight control unit. This relevant factor makes the transition to reality smoother and easier. Controllers using behavior trees were evolved using a developed evolutionary algorithm, and several experiments were conducted. Results demonstrated that it is possible to evolve a robotic controller in realistic simulation environments, using a simulated drone model that exists in the real world, and also the same flight control unit and operating system that is generally used in real world experiments.Atualmente os Veículos Não Tripulados (VNT) encontram-se difundidos por todo o Mundo. A maioria destes veículos requerem um elevado controlo humano, e o sucesso das missões está diretamente dependente deste fator. Assim, é importante utilizar técnicas de aprendizagem automática que irão treinar os controladores dos VNT, de modo a automatizar o controlo, tornando o processo mais eficiente. As estratégias evolutivas podem ser a chave para uma aprendizagem robusta e adaptativa em sistemas robóticos. Vários estudos têm sido realizados nos últimos anos, contudo, existem lacunas que precisam de ser abordadas, tais como o reality gap. Este facto ocorre quando os controladores treinados em ambientes simulados falham ao serem transferidos para VNT reais. Este trabalho propõe uma abordagem para a resolução de missões com VNT, utilizando um simulador realista e estratégias evolutivas para treinar controladores. A arquitetura escolhida é facilmente escalável para sistemas com múltiplos VNT. Nesta tese, é apresentada a arquitetura e configuração do ambiente de simulação, incluindo o modelo e software de simulação do VNT. O modelo de VNT escolhido para as simulações é um modelo real e amplamente utilizado, assim como o software e a unidade de controlo de voo. Este fator é relevante e torna a transição para a realidade mais suave. É desenvolvido um algoritmo evolucionário para treinar um controlador, que utiliza behavior trees, e realizados diversos testes. Os resultados demonstram que é possível evoluir um controlador em ambientes de simulação realistas, utilizando um VNT simulado mas real, assim como utilizando as mesmas unidades de controlo de voo e software que são amplamente utilizados em ambiente real

Differential Particle Swarm Evolution for Robot Control Tuning

We present a differential particle swarm evolution (DPSE) algorithm which combines the basic idea of velocity and position update rules from particle swarm optimization (PSO) and the concept of differential mutation from differential evolution (DE) in a new way. With the goal of optimizing within a limited number of function evaluations, the algorithm is tested and compared with the standard PSO and DE methods on 14 benchmark problems to illustrate that DPSE has the potential to achieve a faster convergence and a better solution. Simulation results show that, on the average, DPSE outperforms DE by 39.20% and PSO by 14.92% on the 14 benchmark problems. To show the feasibility of the proposed strategy on a real-world optimization problem, an application of DPSE to optimize the parameters of active disturbance rejection control (ADRC) in PUMA-560 robot is presented

Differential Particle Swarm Evolution for Robot Control Tuning

We present a differential particle swarm evolution (DPSE) algorithm which combines the basic idea of velocity and position update rules from particle swarm optimization (PSO) and the concept of differential mutation from differential evolution (DE) in a new way. With the goal of optimizing within a limited number of function evaluations, the algorithm is tested and compared with the standard PSO and DE methods on 14 benchmark problems to illustrate that DPSE has the potential to achieve a faster convergence and a better solution. Simulation results show that, on the average, DPSE outperforms DE by 39.20% and PSO by 14.92% on the 14 benchmark problems. To show the feasibility of the proposed strategy on a real-world optimization problem, an application of DPSE to optimize the parameters of active disturbance rejection control (ADRC) in PUMA-560 robot is presented