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The island model for evolutionary algorithms allows to delay the global convergence of the evolution process and encourage diversity. However, solving large size and time-intensive combinatorial optimization problems with the island model requires a large amount of computational resources. GPU computing is recently revealed as a powerful way to harness(More)
—The quadratic 3-dimensional assignment problem (Q3AP) is an extension of the well-known NP-hard quadratic assignment problem. It has been proved to be one of the most difficult combinatorial optimization problems. Local search (LS) algorithms are a class of heuristics which have been successfully applied to solve such hard optimization problem. These(More)
—Local search metaheuristics (LSMs) are efficient methods for solving complex problems in science and industry. They allow significantly to reduce the size of the search space to be explored and the search time. Nevertheless, the resolution time remains prohibitive when dealing with large problem instances. Therefore, the use of GPU-based massively parallel(More)
— 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(More)
Local search (LS) algorithms are among the most powerful techniques for solving computationally hard problems in combinatorial optimization. These algorithms could be viewed as " walks through neighborhoods " where the walks are performed by iterative procedures that allow to move from a solution to another one in the solution space. In these heuristics,(More)
Optimization problems are more and more complex and their resource requirements are ever increasing. Although metaheuristics allow to significantly reduce the computational complexity of the search process , the latter remains time-consuming for many problems in diverse domains of application. As a result, the use of GPU has been recently revealed as an(More)
Local search (LS) algorithms are among the most powerful techniques for solving com-putationally hard problems in combinatorial optimization. These algorithms could be viewed as " walks through neighborhoods " where the walks are performed by iterative procedures that allow to move from a solution to another one in the solution space. In these heuristics,(More)
In practice, combinatorial optimization problems are complex and computationally time-intensive. Local search algorithms are powerful heuristics which allow to significantly reduce the computation time cost of the solution exploration space. In these algorithms, the multi-start model may improve the quality and the robustness of the obtained solutions.(More)
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In this paper, we propose a pioneering framework called ParadisEO-MO-GPU for the reusable design and implementation of parallel local search metaheuristics (S- Metaheuristics)on Graphics Processing Units (GPU). We revisit the ParadisEO-MO software framework to allow its utilization on GPU accelerators focusing on the parallel iteration-level model, the(More)