A parallel 2-opt algorithm for the traveling salesman problem
Future Generation Computer Systems - Special issue: massive parallel computing
Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
ECGA vs. BOA in discovering stock market trading experts
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Discovering stock market trading rules using multi-layer perceptrons
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
A highly-parallel TSP solver for a GPU computing platform
NMA'10 Proceedings of the 7th international conference on Numerical methods and applications
CHC-based algorithms for the dynamic traveling salesman problem
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
Infeasibility driven evolutionary algorithm with ARIMA-based prediction mechanism
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
A predictive evolutionary algorithm for dynamic constrained inverse kinematics problems
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Extended virtual loser genetic algorithm for the dynamic traveling salesman problem
Proceedings of the 15th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
This paper presents a massively parallel evolutionary algorithm with local search mechanism dedicated to dynamic optimization. Its application for solving Dynamic Traveling Salesman Problem (DTSP) is discussed. The algorithm is designed for many-core graphics processors with the Compute Unified Device Architecture (CUDA), which is a parallel computing architecture for nVidia graphics processors. Experiments on a number of benchmark DTSP problems confirmed the efficiency of the algorithm and the parallel computing model designed.