Grid computing for parallel bioinspired algorithms
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Stochastic Local Search Algorithms for Multiobjective Combinatorial Optimizations: Methods and Analysis
Evolutionary Computing on Consumer Graphics Hardware
IEEE Intelligent Systems
A data parallel approach to genetic programming using programmable graphics hardware
Proceedings of the 9th annual conference on Genetic and evolutionary computation
An Efficient Fine-grained Parallel Genetic Algorithm Based on GPU-Accelerated
NPC '07 Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops
Program optimization carving for GPU computing
Journal of Parallel and Distributed Computing
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Solving very large instances of the scheduling of independent tasks problem on the GPU
Journal of Parallel and Distributed Computing
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Multiobjective local search algorithms are efficient methods to solve complex problems in science and industry. Even if these heuristics allow to significantly reduce the computational time of the solution search space exploration, this latter cost remains exorbitant when very large problem instances are to be solved. As a result, the use of graphics processing units (GPU) has been recently revealed as an efficient way to accelerate the search process. This paper presents a new methodology to design and implement efficiently GPU-based multiobjective local search algorithms. The experimental results show that the approach is promising especially for large problem instances.