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In this paper, we describe an implementation of Node Histogram Sampling Algorithm (NHBSA) on GPUs with CUDA and apply the algorithm to solve large scale QAP instances. To solve large scale QAP instances, we combined the taboo search with NHBSA. In this implementation, we used an efficient thread assignment method, Move-Cost Adjusted Thread Assignment (MATA), which is proposed in a previous study....
This paper focuses on solving large size optimization problems using GPGPU. Evolutionary Algorithms for solving these optimization problems suffer from the curse of dimensionality, which implies that their performance deteriorates as quickly as the dimensionality of the search space increases. This difficulty makes very challenging the performance studies for very high dimensional problems. Furthermore,...
In the paper, particle gradient multi-objective evolutionary algorithm (PGMOEA) on GPU is presented. PGMOEA extends the classical particle dynamic multi-objective evolutionary algorithm by incorporating the gradient information of each particle from evolutionary programming. We perform experiments to compare PGMOEA on GPU with PGMOEA on CPU and demonstrate that PGMOEA on GPU is much more effective...
Metaheuristics are used for solving optimization problems since they are able to compute near optimal solutions in reasonable times. However, solving large instances it may pose a challenge even for these techniques. For this reason, metaheuristics parallelization is an interesting alternative in order to decrease the execution time and to provide a different search pattern. In the last years, GPUs...
An important and challenging data mining application in marketing is to learn models for predicting potential customers who contribute large profit to a company under resource constraints. In this paper, we first formulate this learning problem as a constrained optimization problem and then converse it to an unconstrained Multi-objective Optimization Problem (MOP). A parallel Multi-Objective Evolutionary...
Over the last years, interest in hybrid meta-heuristics has risen considerably in the field of optimization. Combinations of methods such as evolutionary algorithms and local searches have provided very powerful search algorithms. However, due to their complexity, the computational time of the solution search exploration remains exorbitant when large problem instances are to be solved. Therefore,...
Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of Graphics Processing Units (GPU) and the Compute Unified Device Architecture (CUDA) platform. In case of Evolutionary algorithms, which are inherently parallel, this technology may be advantageous for running experiments demanding high computing time. In this paper, we provide an implementation...
In this paper, an automatic planning system is developed. By our proposed technique, the vehicle routing planning is optimized using the evolutionary computation. A mutation which sa-tisfies all constraints is designed. Simulation results using real world data show the effectiveness of our developed system. Furthermore, in order to improve in the computational speed by paralleliza-tion, using GPGPU...
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