10,240 research outputs found
A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems
In this paper, we study various parallelization schemes for the Variable
Neighborhood Search (VNS) metaheuristic on a CPU-GPU system via OpenMP and
OpenACC. A hybrid parallel VNS method is applied to recent benchmark problem
instances for the multi-product dynamic lot sizing problem with product returns
and recovery, which appears in reverse logistics and is known to be NP-hard. We
report our findings regarding these parallelization approaches and present
promising computational results.Comment: 8 pages, 1 figur
A Parallel Monte-Carlo Tree Search-Based Metaheuristic For Optimal Fleet Composition Considering Vehicle Routing Using Branch & Bound
In this paper, a Monte-Carlo Tree Search (MCTS)-based metaheuristic is
developed that guides a Branch & Bound (B&B) algorithm to find the globally
optimal solution to the heterogeneous fleet composition problem while
considering vehicle routing. Fleet Size and Mix Vehicle Routing Problem with
Time Windows (FSMVRPTW). The metaheuristic and exact algorithms are implemented
in a parallel hybrid optimization algorithm where the metaheuristic rapidly
finds feasible solutions that provide candidate upper bounds for the B&B
algorithm which runs simultaneously. The MCTS additionally provides a candidate
fleet composition to initiate the B&B search. Experiments show that the
proposed approach results in significant improvements in computation time and
convergence to the optimal solution.Comment: Submitted to the IEEE Intelligent Vehicles Symposium 202
A cooperative parallel metaheuristic for the capacitated vehicle routing problem
This paper introduces a cooperative parallel metaheuristic for the capacitated vehicle routing problem. The proposed metaheuristic consists of multiple parallel tabu search threads that cooperate by asynchronously exchanging best-found solutions through a common solution pool. The solutions sent to the pool are clustered according to their similarities. The search history information identified from the solution clusters is applied to guide the intensification or diversification of the tabu search threads. Computational experiments on two sets of large-scale benchmark instance sets from the literature demonstrate that the suggested metaheuristic is highly competitive, providing new best solutions to ten of those well-studied instances.acceptedVersio
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
A WOA-based optimization approach for task scheduling in cloud Computing systems
Task scheduling in cloud computing can directly
affect the resource usage and operational cost of a system. To
improve the efficiency of task executions in a cloud, various
metaheuristic algorithms, as well as their variations, have been
proposed to optimize the scheduling. In this work, for the
first time, we apply the latest metaheuristics WOA (the whale
optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that
basis, we propose an advanced approach called IWC (Improved
WOA for Cloud task scheduling) to further improve the optimal
solution search capability of the WOA-based method. We present
the detailed implementation of IWC and our simulation-based
experiments show that the proposed IWC has better convergence
speed and accuracy in searching for the optimal task scheduling
plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource
utilization, in the presence of both small and large-scale tasks
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