Thomas Gabel

Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm

Path planning algorithms have been used in different applications with the aim of finding a suitable collision-free path which satisfies some certain criteria such as the shortest path length and smoothness; thus, defining a suitable curve to describe path is essential. The main goal of these algorithms is to find the shortest and smooth path between the starting and target points. This paper makes use of a Bézier curve-based model for path planning. The control points of the Bézier curve significantly influence the length and smoothness of the path. In this paper, a novel Chaotic Particle Swarm Optimization (CPSO) algorithm has been proposed to optimize the control points of Bézier curve, and the proposed algorithm comes in two variants: CPSO-I and CPSO-II. Using the chosen control points, the optimum smooth path that minimizes the total distance between the starting and ending points is selected. To evaluate the CPSO algorithm, the results of the CPSO-I and CPSO-II algorithms are compared with the standard PSO algorithm. The experimental results proved that the proposed algorithm is capable of finding the optimal path. Moreover, the CPSO algorithm was tested against different numbers of control points and obstacles, and the CPSO algorithm achieved competitive results.

MOGOA algorithm for constrained and unconstrained multi-objective optimization problems

Grasshopper Optimization Algorithm (GOA) was modified in this paper, to optimize multi-objective problems, and the modified version is called Multi-Objective Grasshopper Optimization Algorithm (MOGOA). An external archive is integrated with the GOA for saving the Pareto optimal solutions. The archive is then employed for defining the social behavior of the GOA in the multi-objective search space. To evaluate and verify the effectiveness of the MOGOA, a set of standard unconstrained and constrained test functions are used. Moreover, the proposed algorithm was compared with three well-known optimization algorithms: Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Ant Lion Optimizer (MOALO), and Non-dominated Sorting Genetic Algorithm version 2 (NSGA-II); and the obtained results show that the MOGOA algorithm is able to provide competitive results and outperform other algorithms.

Parameter Optimization of Support Vector Machine Using Dragonfly Algorithm

Support Vector Machine (SVM) parameters such as penalty and kernel parameters have a great influence on the complexity and accuracy of the classification model. In this paper, Dragonfly algorithm (DA) has been proposed to optimize the parameters of SVM; thus, the classification error can be decreased. To evaluate the proposed model (DA-SVM), the experiment adopted six standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the DA-SVM algorithm are compared with two well-known optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results demonstrated that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem.

Progress in RoboCup Revisited: The State of Soccer Simulation 2D

A remarkable feature of RoboCup’s soccer simulation leagues is their ability to quantify and prove the exact progress made over years. In this paper, we present and discuss the results of an extensive empirical study of the progress and the currently reached state of 2D soccer simulation. Our main finding is that the current decade has witnessed a continuous and statistically significant improvement of the overall level of play, but that the magnitude of the progress made has dropped clearly when compared to the previous decade. In accordance to this, we envision possible future prospects for the 2D league that might respond to our empirical findings.

Top-Down Induction of Similarity Measures Using Similarity Clouds

The automatic acquisition of a similarity measure for a CBR system is appealing as it frees the system designer from the tedious task of defining it manually. However, acquiring similarity measures with some machine learning approach typically results in some black box representation of similarity whose magic-like combination of high precision and low explainability may decrease a human user’s trust in the system. In this paper, we target this problem by suggesting a method to induce a human-readable and easily understandable – and thus potentially trustworthy – representation of similarity from a previously learned black box-like representation of similarity measures. Our experimental evaluations support the claim that, given some highly precise learned similarity measure, we can induce a less powerful, but human-understandable representation of it while its corresponding level of accuracy is only marginally impaired.

Eavesdropping Opponent Agent Communication Using Deep Learning

We present a method for learning to interpret and understand foreign agent communication. Our approach is based on casting the contents of intercepted opponent agent communication to a bit-level representation and on training and employing deep convolutional neural networks for decoding the meaning of received messages. We empirically evaluate our method on real-world data acquired from the multi-agent domain of robotic soccer simulation, demonstrating the effectiveness and robustness of the learned decoding models.

Classification of Toxicity Effects of Biotransformed Hepatic Drugs Using Optimized Support Vector Machine

Measuring toxicity is an important step in drug development, and there is a high demand to develop computational models that can predict the drug toxicity risks. In this study, we used a dataset that consists of 553 drug samples that biotransformed in liver. The toxic effects were calculated for the current data are mutagenic, tumorigenic, irritant, and reproductive effects. The proposed model has two phases, in the first phase; sampling algorithms were utilized to solve the problem of imbalanced dataset, in the second phase, the Support Vector Machines (SVM) classifier was used to classify an unknown drug sample into toxic or non-toxic. Moreover, in our model, Dragonfly Algorithm (DA) was used to optimize SVM parameters such as the penalty parameter and kernel parameters. The experimental results demonstrated that the proposed model obtained high sensitivity to all toxic effects, which indicates that it could be used for the prediction of drug toxicity in the early stage of drug development.

Combining evolutionary algorithms and case-based reasoning for learning high-quality shooting strategies in AI birds

Self-adaptation and the ability to assimilate new knowledge are two fundamental characteristics of intelligent systems. In this paper we leverage methods from evolutionary optimization and from case-based reasoning to construct an agent that is able to evolve in such a way that it is able to successfully master the popular video game Angry Birds.