Hanne, Thomas
E-Mail-Adresse
Geburtsdatum
Suchergebnisse
Optimization of artificial landscapes with a hybridized firefly algorithm
This paper shows how the metaheuristic Firefly Algorithm (FA) can be enhanced by hybridization with a genetic algorithm to achieve better results for optimization problems. The authors examine which configuration of the hybridized FA performs best during a number of computational tests. The performance of the hybrid FA is compared with that of the regular FA in solving test functions for single-objective optimization problems in two and n-dimensional spaces. The key findings are that more complex optimization problems benefit from the hybrid FA because it outperforms the basic FA. In addition, some useful parameters settings for the suggested algorithm are determined.
Benchmarking tabu search and simulated annealing for the capacitated vehicle routing problem
This paper addresses the Capacitated Vehicle Routing Problem (CVRP) consisting of a single depot and several customers that are supplied with goods by capacitated vehicles from a depot. The main objective of the vehicle routing problem is to minimize the traveled distance of all vehicles. We compare the Tabu Search (TS) and Simulated Annealing (SA) algorithm with different initial solution strategies to solve the CVRP. We run the publicly available solver on a set of benchmark problems comparing above mentioned methods and initial solutions. The results show that TS appears superior for small-sized problems, while SA has an advantage for mid-sized problems. For larger problems the preferability of a methods depends on the available run time with SA appear promising for shorter runtime and TS for longer.
A multi-threaded cuckoo search algorithm for the capacitated vehicle routing problem
Robotic path planning by Q learning and a performance comparison with classical path finding algorithms
Q Learning is a form of reinforcement learning for path finding problems that does not require a model of the environment. It allows the agent to explore the given environment and the learning is achieved by maximizing the rewards for the set of actions it takes. In the recent times, Q Learning approaches have proven to be successful in various applications ranging from navigation systems to video games. This paper proposes a Q learning based method that supports path planning for robots. The paper also discusses the choice of parameter values and suggests optimized parameters when using such a method. The performance of the most popular path finding algorithms such as A* and Dijkstra algorithm have been compared to the Q learning approach and were able to outperform Q learning with respect to computation time and resulting path length.
Naïve Bayes and named entity recognition for requirements mining in job postings
This paper analyses how the required skills in a job post can be extracted. With an automated extraction of skills from unstructured text, applicants could be more accurately matched and search engines could provide better recommendations. The problem is optimized by classifying the relevant parts of the description with a multinomial naïve Bayes model. The model identifies the section of the unstructured text in which the requirements are stated. Subsequently, a named entity recognition (NER) model extracts the required skills from the classified text. This approach minimizes the false positives since the data which is analyzed is already filtered. The results show that the naïve Bayes model classifies up to 99% of the sections correctly, and the NER model extracts 65% of the skills required for a position. The accuracy of the NER model is not sufficient to be used in production. On the validation set, the performance was insufficient. A more consistent labelling guideline would be needed and more data should be annotated to increase the performance.
Improved path planning with memory efficient A* algorithm and optimization of narrow passages
Quantum computing in supply chain management state of the art and research directions
Quantum computing is the most promising computational advance of the coming decade for solving the most challenging problems in supply chain management and logistics. This paper reviews the state-of-the-art of quantum computing and provides directions for future research. First, general concepts relevant to quantum computers and quantum computing are introduced. Second, the dominating quantum technologies are presented. Third, the quantum industry is analyzed, and recent applications in different fields of supply chain management and logistics are illustrated. Fourth, directions for future research are given. We hope this review to educate and inspire the use of quantum computing in the fields of optimization, artificial intelligence, and machine learning for supply chain and logistics.
Parameter selection for ant colony optimization for solving the travelling salesman problem based on the problem size
powerGhosts & defensiveGhosts – Enhanced ghost team controller based on Ant Colony Optimization for Ms. Pac-Man
This paper presents an improved controller based on Ant Colony Optimization for the ghost team of Ms. Pac-Man. The controller is an enhanced version of fairGhosts which is based on the ghostAnt framework. Various improvements were implemented in terms of parameters and concepts. Especially for the explorer ants, fairGhosts uses a simplified version, as the exact reasoning for the proposed concepts could not be determined in the ghostAnt framework. In this paper, two new types of ghost teams are proposed after the modifications were conducted: powerGhosts and defensiveGhosts. powerGhosts take into account the power pill aspect for stopping criteria and solution quality of the explorer ants, and defensiveGhosts additionally involve a threshold of Ms. Pac-Man’s distance to the nearest power pill for the hunter ants, so as not to be caught easily. Test results show that the powerGhosts version shows on average 15% better results than the initial fairGhosts setup, while defensiveGhosts performs equal or slightly worse than the initial implementation. It can be concluded that including the power pill aspect in the explorer ants concept shows an improved performance of the ghost team. On the other hand, the concept of distancing ghosts from Ms. Pac-Man when she is near a power pill did not result in any significant improvement.